EU-AIMS ADI-R Subtyping Connectivity Analysis

easypackages::libraries(c("here","ggplot2","nlme","readxl","matlabr","circlize","scico"))
source("/Users/mlombardo/Dropbox/GitHubRepos/utils/cohens_d.R")
source("/Users/mlombardo/Dropbox/R/Repfunctionspack6.R")
source("/Users/mlombardo/Dropbox/R/get_ggColorHue.R")
fdr_thresh = 0.05

options(matlab.path = "/Applications/MATLAB_R2019b.app/bin")

rootpath = "/Users/mlombardo/Dropbox/euaims/data/adir"
datapath = here("data")
codepath = here("code")
resultpath = here("results")
plotpath = here("plots")

Run the MATLAB script that estimates the partial correlations

RUNMATLAB = FALSE

if (RUNMATLAB){
  # z = 0.5
  code2run = sprintf("cd %s; estimateConnectivity_z05('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 0.6
  code2run = sprintf("cd %s; estimateConnectivity_z06('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 0.7
  code2run = sprintf("cd %s; estimateConnectivity_z07('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)

  # z = 0.8
  code2run = sprintf("cd %s; estimateConnectivity_z08('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 0.9
  code2run = sprintf("cd %s; estimateConnectivity_z09('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
  
  # z = 1
  code2run = sprintf("cd %s; estimateConnectivity_z1('%s',0,1);",codepath,"ridge")
  res = run_matlab_code(code2run)
}

Main analysis - Z = 0.5

# Z threshold
z_thresh = 0.5

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")


vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")
  
#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")

    
    #--------------------------------------------------------------------------
    # Discovery 
    
    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC06 IC01_IC06                   1.8413429                0.067533513
## IC01_IC17 IC01_IC17                   1.5666812                0.119282487
## IC03_IC12 IC03_IC12                   2.8401768                0.005131071
## IC03_IC13 IC03_IC13                   2.2147343                0.028276159
## IC07_IC13 IC07_IC13                  -2.8083624                0.005637955
## IC08_IC13 IC08_IC13                  -0.3378935                0.735912773
## IC12_IC17 IC12_IC17                   1.2111391                0.227734188
## IC13_IC14 IC13_IC14                  -1.8206843                0.070634676
## IC14_IC16 IC14_IC16                  -3.1379365                0.002046295
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC06              -0.31589407                  44.16421
## IC01_IC17              -0.31085336                  42.95287
## IC03_IC12              -0.52491355                  36.32295
## IC03_IC13              -0.40328866                 -66.73545
## IC07_IC13               0.46427323                 -35.40795
## IC08_IC13               0.05698519                 -82.82008
## IC12_IC17              -0.22269681                  53.46934
## IC13_IC14               0.32622066                -183.60601
## IC14_IC16               0.59459741                -186.70432
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC06                  62.42476                 2.65289954
## IC01_IC17                  61.21342                 2.18232049
## IC03_IC12                  54.58350                 2.82566015
## IC03_IC13                 -48.47490                 1.43933204
## IC07_IC13                 -17.14740                -2.97423234
## IC08_IC13                 -64.55952                -0.08929899
## IC12_IC17                  71.72989                 1.03087246
## IC13_IC14                -165.34546                -2.32057814
## IC14_IC16                -168.44377                -2.43350707
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC06               0.008789920            -0.442202657
## IC01_IC17               0.030553203            -0.378509720
## IC03_IC12               0.005323903            -0.495574986
## IC03_IC13               0.152022265            -0.274770515
## IC07_IC13               0.003395328             0.528531223
## IC08_IC13               0.928956676             0.009370899
## IC12_IC17               0.304166174            -0.185987687
## IC13_IC14               0.021580205             0.391666635
## IC14_IC16               0.016061402             0.410320007
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC06                59.560245                 78.12275       19.4590668
## IC01_IC17                 9.116484                 27.67898        6.8012016
## IC03_IC12                33.917730                 52.48023       33.8497301
## IC03_IC13              -131.336237               -112.77374        1.5748951
## IC07_IC13               -97.179641                -78.61714       51.4605481
## IC08_IC13              -114.633554                -96.07105        0.6747666
## IC12_IC17                26.210240                 44.77274        1.1459067
## IC13_IC14              -167.216098               -148.65360        9.4304914
## IC14_IC16              -171.385723               -152.82322       10.4387817
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC06                  0.7572340               0.449842834
## IC01_IC17                  0.4593805               0.646483354
## IC03_IC12                  2.0187698               0.044909445
## IC03_IC13                  2.6093897               0.009788873
## IC07_IC13                 -1.6891351               0.092825290
## IC08_IC13                  1.1404445               0.255529170
## IC12_IC17                  2.3199351               0.021401223
## IC13_IC14                 -2.0447737               0.042249033
## IC14_IC16                 -2.4362432               0.015757921
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC06             -0.08383745                 15.16530
## IC01_IC17             -0.05621161                 90.02013
## IC03_IC12             -0.28401316                 50.42322
## IC03_IC13             -0.36665348               -105.62195
## IC07_IC13              0.22091396                -58.55765
## IC08_IC13             -0.17852219               -110.99064
## IC12_IC17             -0.34180496                 56.50485
## IC13_IC14              0.29431425               -234.25081
## IC14_IC16              0.33880867               -236.85212
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC06                 34.80330                -0.2030723
## IC01_IC17                109.65813                 3.0806995
## IC03_IC12                 70.06122                 1.2376225
## IC03_IC13                -85.98396                 2.7158706
## IC07_IC13                -38.91965                -3.5377902
## IC08_IC13                -91.35264                 2.6455114
## IC12_IC17                 76.14285                 3.4929224
## IC13_IC14               -214.61282                -2.5268818
## IC14_IC16               -217.21412                -2.0457879
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC06             0.8392902313             0.03609126
## IC01_IC17             0.0023635282            -0.40852891
## IC03_IC12             0.2173442205            -0.17653043
## IC03_IC13             0.0072039804            -0.38221938
## IC07_IC13             0.0005042745             0.49892313
## IC08_IC13             0.0088220325            -0.37715315
## IC12_IC17             0.0005910705            -0.46184760
## IC13_IC14             0.0123022871             0.36008469
## IC14_IC16             0.0421198533             0.30492918
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC06               64.847220                84.60705       0.5685076
## IC01_IC17               22.430173                42.19000      13.8433937
## IC03_IC12                8.525016                28.28485       1.2856932
## IC03_IC13             -162.616305              -142.85648      26.4684802
## IC07_IC13              -90.490429               -70.73060     137.8846483
## IC08_IC13             -104.859512               -85.09968      12.8698565
## IC12_IC17               18.490888                38.25072     194.6394824
## IC13_IC14             -233.776752              -214.01692      15.5978769
## IC14_IC16             -220.270275              -200.51045       5.3511454
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC06                         0.96044794                         0.3389555
## IC01_IC17                         0.96148792                         0.3384350
## IC03_IC12                         1.11565258                         0.2670245
## IC03_IC13                         0.07808261                         0.9379055
## IC07_IC13                        -0.99012090                         0.3243079
## IC08_IC13                        -1.11081930                         0.2690905
## IC12_IC17                        -0.61611145                         0.5391055
## IC13_IC14                        -0.22061138                         0.8258077
## IC14_IC16                        -1.31139157                         0.1924812
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC06                     -0.22890262                        52.107351
## IC01_IC17                     -0.20401823                        84.797584
## IC03_IC12                     -0.21724045                        49.153699
## IC03_IC13                     -0.04431897                       -29.145006
## IC07_IC13                      0.23597736                        -9.320828
## IC08_IC13                      0.22505132                       -36.378068
## IC12_IC17                      0.12293432                        42.704912
## IC13_IC14                      0.02991108                      -156.737603
## IC14_IC16                      0.23486204                      -117.257104
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC06                        68.418344                        2.70460822
## IC01_IC17                       101.108577                       -0.43809266
## IC03_IC12                        65.464693                        1.71860081
## IC03_IC13                       -12.834013                       -0.60853590
## IC07_IC13                         6.990165                        0.04114173
## IC08_IC13                       -20.067075                       -2.11013008
## IC12_IC17                        59.015905                       -1.45310873
## IC13_IC14                      -140.426610                        0.01430202
## IC14_IC16                      -100.946110                       -0.42565084
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC06                      0.007841698                    -0.52670919
## IC01_IC17                      0.662113323                     0.05297288
## IC03_IC12                      0.088287598                    -0.29435833
## IC03_IC13                      0.543991934                     0.11211109
## IC07_IC13                      0.967251859                    -0.01689075
## IC08_IC13                      0.036941747                     0.38391122
## IC12_IC17                      0.148825533                     0.25602360
## IC13_IC14                      0.988612979                     0.05172973
## IC14_IC16                      0.671131108                     0.11437242
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC06                        30.41902                       47.243145
## IC01_IC17                        25.04445                       41.868580
## IC03_IC12                        61.70028                       78.524406
## IC03_IC13                       -78.68936                      -61.865238
## IC07_IC13                       -19.04316                       -2.219033
## IC08_IC13                       -48.05103                      -31.226908
## IC12_IC17                        43.39973                       60.223854
## IC13_IC14                      -139.32191                     -122.497785
## IC14_IC16                      -139.51822                     -122.694091
##           SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC06                    12.6815102
## IC01_IC17                     0.4576832
## IC03_IC12                     2.7826665
## IC03_IC13                     0.7371487
## IC07_IC13                     0.5170103
## IC08_IC13                     5.0438772
## IC12_IC17                     1.6917758
## IC13_IC14                     0.6741652
## IC14_IC16                     0.6009812
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC06 IC01_IC06       19.4590668       0.5685076
## IC01_IC17 IC01_IC17        6.8012016      13.8433937
## IC03_IC12 IC03_IC12       33.8497301       1.2856932
## IC03_IC13 IC03_IC13        1.5748951      26.4684802
## IC07_IC13 IC07_IC13       51.4605481     137.8846483
## IC08_IC13 IC08_IC13        0.6747666      12.8698565
## IC12_IC17 IC12_IC17        1.1459067     194.6394824
## IC13_IC14 IC13_IC14        9.4304914      15.5978769
## IC14_IC16 IC14_IC16       10.4387817       5.3511454
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.6

# Z threshold
z_thresh = 0.6

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis 
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17                  0.81720629                0.414988961
## IC03_IC13 IC03_IC13                  1.90127132                0.059009568
## IC05_IC06 IC05_IC06                 -0.84777062                0.397793817
## IC07_IC13 IC07_IC13                 -2.84450243                0.005011569
## IC08_IC13 IC08_IC13                  0.37624584                0.707217480
## IC12_IC17 IC12_IC17                  0.78248573                0.435050702
## IC13_IC14 IC13_IC14                 -1.77833953                0.077189666
## IC14_IC20 IC14_IC20                 -0.01674859                0.986657422
## IC17_IC18 IC17_IC18                  2.87509709                0.004571807
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17             -0.146474183                  53.93181
## IC03_IC13             -0.305211994                 -82.08286
## IC05_IC06              0.140469299                 142.61675
## IC07_IC13              0.442163838                 -46.02786
## IC08_IC13             -0.063945163                 -92.92388
## IC12_IC17             -0.128015783                  63.64917
## IC13_IC14              0.276833395                -206.62311
## IC14_IC20              0.005368302                -174.10229
## IC17_IC18             -0.462014386                  63.09198
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17                  72.71120                 1.96934450
## IC03_IC13                 -63.30346                 1.71410891
## IC05_IC06                 161.39614                -2.14851748
## IC07_IC13                 -27.24847                -2.93772770
## IC08_IC13                 -74.14449                -0.08528237
## IC12_IC17                  82.42856                 0.95503909
## IC13_IC14                -187.84372                -2.75453695
## IC14_IC20                -155.32290                 2.05358856
## IC17_IC18                  81.87137                 1.85656428
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17               0.050557979            -0.309488139
## IC03_IC13               0.088352994            -0.307641538
## IC05_IC06               0.033104923             0.338954646
## IC07_IC13               0.003770877             0.479576719
## IC08_IC13               0.932138454             0.002715119
## IC12_IC17               0.340930126            -0.175577118
## IC13_IC14               0.006525459             0.436367512
## IC14_IC20               0.041565450            -0.314145999
## IC17_IC18               0.065124293            -0.286659721
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17                 17.74967                 36.63463        3.4984836
## IC03_IC13               -128.19376               -109.30879        2.9950345
## IC05_IC06                113.59895                132.48392        4.5945424
## IC07_IC13                -96.57675                -77.69178       47.8441467
## IC08_IC13               -112.97042                -94.08546        0.6678915
## IC12_IC17                 23.51318                 42.39815        1.1006862
## IC13_IC14               -181.97684               -163.09187       23.3826440
## IC14_IC20               -179.30575               -160.42079        1.9888481
## IC17_IC18                 38.58658                 57.47155        2.9514062
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17                 0.67056488               0.503343146
## IC03_IC13                 2.92464289               0.003884712
## IC05_IC06                -0.02340494               0.981352762
## IC07_IC13                -1.65273052               0.100100326
## IC08_IC13                 0.82752646               0.409016539
## IC12_IC17                 2.22369981               0.027392033
## IC13_IC14                -2.23469322               0.026647323
## IC14_IC20                 2.06411072               0.040418172
## IC17_IC18                 3.11360312               0.002145382
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17            -0.101073385                 84.90317
## IC03_IC13            -0.425695383               -100.37980
## IC05_IC06             0.007493489                129.15594
## IC07_IC13             0.208090428                -54.16707
## IC08_IC13            -0.124665164               -106.96793
## IC12_IC17            -0.335209138                 60.23513
## IC13_IC14             0.330565711               -220.17134
## IC14_IC20            -0.318094765               -229.81040
## IC17_IC18            -0.456312568                 49.62148
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17                104.28982                  2.997975
## IC03_IC13                -80.99315                  2.917101
## IC05_IC06                148.54259                 -3.265278
## IC07_IC13                -34.78042                 -3.365334
## IC08_IC13                -87.58127                  2.824398
## IC12_IC17                 79.62178                  3.620622
## IC13_IC14               -200.78469                 -2.151561
## IC14_IC20               -210.42375                  2.328255
## IC17_IC18                 69.00813                  2.816996
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17             0.0030851856             -0.4152045
## IC03_IC13             0.0039634269             -0.4109193
## IC05_IC06             0.0012994901              0.4712827
## IC07_IC13             0.0009267979              0.5026489
## IC08_IC13             0.0052479840             -0.4119680
## IC12_IC17             0.0003778628             -0.4862891
## IC13_IC14             0.0327076560              0.3139276
## IC14_IC20             0.0209622206             -0.3289709
## IC17_IC18             0.0053653571             -0.3846336
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17                26.57670                46.12168       15.663905
## IC03_IC13              -157.12288              -137.57791       45.530629
## IC05_IC06               126.96464               146.50961       10.005277
## IC07_IC13               -90.53141               -70.98644       88.357797
## IC08_IC13              -108.01371               -88.46874       13.635879
## IC12_IC17                17.82539                37.37036      261.142404
## IC13_IC14              -222.96394              -203.41897        6.906068
## IC14_IC20              -286.52698              -266.98201       10.108331
## IC17_IC18                34.43571                53.98068       33.526075
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17                          0.2291000                         0.8191982
## IC03_IC13                         -0.6874310                         0.4931952
## IC05_IC06                         -0.7896305                         0.4313687
## IC07_IC13                         -1.1931590                         0.2352629
## IC08_IC13                         -0.3676063                         0.7138425
## IC12_IC17                         -1.0690293                         0.2872952
## IC13_IC14                          0.3335995                         0.7392893
## IC14_IC20                         -1.4795578                         0.1417255
## IC17_IC18                          0.1320008                         0.8952143
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17                     -0.03143307                         87.83246
## IC03_IC13                      0.11255443                        -38.73921
## IC05_IC06                      0.13762120                        104.91304
## IC07_IC13                      0.22470100                        -15.54993
## IC08_IC13                      0.05857350                        -43.07923
## IC12_IC17                      0.20255134                         56.69954
## IC13_IC14                     -0.07479305                       -166.27256
## IC14_IC20                      0.27635916                        -92.14238
## IC17_IC18                     -0.02978529                         11.97092
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17                       104.456568                        -0.6936942
## IC03_IC13                       -22.115098                        -0.5383401
## IC05_IC06                       121.537143                         0.8769089
## IC07_IC13                         1.074179                         0.2070384
## IC08_IC13                       -26.455127                        -2.3133527
## IC12_IC17                        73.323644                        -1.6693701
## IC13_IC14                      -149.648449                        -0.5669362
## IC14_IC20                       -75.518276                         0.1377518
## IC17_IC18                        28.595025                        -0.4742845
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17                       0.48920358                     0.11397366
## IC03_IC13                       0.59133121                     0.09498840
## IC05_IC06                       0.38227381                    -0.13381778
## IC07_IC13                       0.83632789                    -0.04431073
## IC08_IC13                       0.02239135                     0.40635080
## IC12_IC17                       0.09762934                     0.29086760
## IC13_IC14                       0.57180775                     0.14438115
## IC14_IC20                       0.89066554                    -0.04543209
## IC17_IC18                       0.63615184                     0.09394860
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17                        36.53677                       53.458457
## IC03_IC13                       -70.34723                      -53.425542
## IC05_IC06                       107.92575                      124.847441
## IC07_IC13                       -19.26034                       -2.338646
## IC08_IC13                       -51.02409                      -34.102400
## IC12_IC17                        42.32059                       59.242281
## IC13_IC14                      -143.39561                     -126.473918
## IC14_IC20                      -106.13772                      -89.216032
## IC17_IC18                        44.09157                       61.013262
##           SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17                     0.7214381
## IC03_IC13                     0.8030154
## IC05_IC06                     0.5145181
## IC07_IC13                     0.4340872
## IC08_IC13                     3.9177261
## IC12_IC17                     2.5739990
## IC13_IC14                     0.6712763
## IC14_IC20                     0.3627529
## IC17_IC18                     0.7152729
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17        3.4984836       15.663905
## IC03_IC13 IC03_IC13        2.9950345       45.530629
## IC05_IC06 IC05_IC06        4.5945424       10.005277
## IC07_IC13 IC07_IC13       47.8441467       88.357797
## IC08_IC13 IC08_IC13        0.6678915       13.635879
## IC12_IC17 IC12_IC17        1.1006862      261.142404
## IC13_IC14 IC13_IC14       23.3826440        6.906068
## IC14_IC20 IC14_IC20        1.9888481       10.108331
## IC17_IC18 IC17_IC18        2.9514062       33.526075
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.7

# Z threshold
z_thresh = 0.7

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC03_IC12 IC03_IC12                   2.5880777                0.010476625
## IC03_IC13 IC03_IC13                   1.7045387                0.090086122
## IC07_IC13 IC07_IC13                  -3.0190862                0.002921424
## IC12_IC17 IC12_IC17                   0.8639704                0.388807371
## IC13_IC14 IC13_IC14                  -1.9487761                0.052949098
## IC17_IC18 IC17_IC18                   3.0694379                0.002492678
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC03_IC12               -0.4064357                  32.02269
## IC03_IC13               -0.2684743                 -91.14486
## IC07_IC13                0.4513105                 -48.05953
## IC12_IC17               -0.1358028                  59.83675
## IC13_IC14                0.2965627                -221.20416
## IC17_IC18               -0.4755712                  60.01583
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC03_IC12                  51.04559                  2.5755098
## IC03_IC13                 -72.12196                  1.8029660
## IC07_IC13                 -29.03662                 -3.2756600
## IC12_IC17                  78.85965                  0.9077748
## IC13_IC14                -202.18126                 -2.9243534
## IC17_IC18                  79.03874                  1.9834594
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC03_IC12               0.010830672              -0.4022660
## IC03_IC13               0.073104214              -0.3102543
## IC07_IC13               0.001269619               0.5095740
## IC12_IC17               0.365238501              -0.1634115
## IC13_IC14               0.003906066               0.4467737
## IC17_IC18               0.048871280              -0.2993575
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC03_IC12                 28.13199                 47.28974        18.381117
## IC03_IC13               -142.87159               -123.71385         3.522744
## IC07_IC13               -106.48364                -87.32590       129.143599
## IC12_IC17                 26.27354                 45.43128         1.058759
## IC13_IC14               -198.61959               -179.46185        37.428061
## IC17_IC18                 39.19986                 58.35761         3.596258
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC03_IC12                   1.874730               0.062477220
## IC03_IC13                   3.109391               0.002185226
## IC07_IC13                  -1.377252               0.170173646
## IC12_IC17                   2.169294               0.031393854
## IC13_IC14                  -2.260699               0.024995687
## IC17_IC18                   2.919683               0.003959615
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC03_IC12              -0.2859527                 55.55774
## IC03_IC13              -0.4665540                -93.48004
## IC07_IC13               0.1782971                -53.80151
## IC12_IC17              -0.3350856                 63.48068
## IC13_IC14               0.3417837               -206.30434
## IC17_IC18              -0.4457264                 52.25844
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC03_IC12                 74.74872                  1.444407
## IC03_IC13                -74.28906                  2.780506
## IC07_IC13                -34.61053                 -3.053138
## IC12_IC17                 82.67166                  3.562849
## IC13_IC14               -187.11336                 -1.854706
## IC17_IC18                 71.44942                  2.486167
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC03_IC12             0.1503440011             -0.2113858
## IC03_IC13             0.0059982978             -0.4022988
## IC07_IC13             0.0026041380              0.4708069
## IC12_IC17             0.0004684346             -0.5011120
## IC13_IC14             0.0652558885              0.2808212
## IC17_IC18             0.0138140386             -0.3457352
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC03_IC12                15.58339                34.93787        1.876880
## IC03_IC13              -145.79806              -126.44358       30.097648
## IC07_IC13               -84.18131               -64.82683       34.851302
## IC12_IC17                16.40916                35.76364      214.801944
## IC13_IC14              -210.19769              -190.84320        3.667604
## IC17_IC18                36.47313                55.82761       13.912669
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC03_IC12                          0.5792453                         0.5635469
## IC03_IC13                         -1.1442064                         0.2548936
## IC07_IC13                         -1.3271076                         0.1870793
## IC12_IC17                         -1.0771983                         0.2836268
## IC13_IC14                          0.2587339                         0.7962991
## IC17_IC18                          0.1335787                         0.8939672
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC03_IC12                     -0.09518514                         49.72572
## IC03_IC13                      0.19476585                        -41.15012
## IC07_IC13                      0.26781460                        -17.22303
## IC12_IC17                      0.20065504                         56.26102
## IC13_IC14                     -0.07483254                       -164.91591
## IC17_IC18                     -0.04763199                         10.89440
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC03_IC12                       66.4004560                        1.14189559
## IC03_IC13                      -24.4753827                       -0.50949046
## IC07_IC13                       -0.5482864                       -0.08776844
## IC12_IC17                       72.9357568                       -1.75752310
## IC13_IC14                     -148.2411724                       -0.85599717
## IC17_IC18                       27.5691412                       -0.07494163
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC03_IC12                       0.25571552                    -0.17433072
## IC03_IC13                       0.61132136                     0.09533090
## IC07_IC13                       0.93020345                     0.00370345
## IC12_IC17                       0.08131659                     0.31020269
## IC13_IC14                       0.39366383                     0.17936131
## IC17_IC18                       0.94038290                     0.04383111
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC03_IC12                        62.47247                       79.490164
## IC03_IC13                       -73.47254                      -56.454847
## IC07_IC13                       -21.93991                       -4.922215
## IC12_IC17                        43.21120                       60.228892
## IC13_IC14                      -147.24087                     -130.223174
## IC17_IC18                        45.73968                       62.757368
##           SCequalRRB_vs_SCoverRRB.repBF
## IC03_IC12                     1.2440002
## IC03_IC13                     0.7123752
## IC07_IC13                     0.4706535
## IC12_IC17                     2.9153373
## IC13_IC14                     0.7414271
## IC17_IC18                     0.6905883
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC03_IC12 IC03_IC12        18.381117        1.876880
## IC03_IC13 IC03_IC13         3.522744       30.097648
## IC07_IC13 IC07_IC13       129.143599       34.851302
## IC12_IC17 IC12_IC17         1.058759      214.801944
## IC13_IC14 IC13_IC14        37.428061        3.667604
## IC17_IC18 IC17_IC18         3.596258       13.912669
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.8

# Z threshold
z_thresh = 0.8

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC17 IC01_IC17                   1.0305727                0.304131713
## IC03_IC13 IC03_IC13                   2.1060507                0.036594355
## IC04_IC12 IC04_IC12                   1.8287645                0.069099104
## IC05_IC06 IC05_IC06                  -1.4268750                0.155357909
## IC07_IC13 IC07_IC13                  -2.7311666                0.006942746
## IC12_IC17 IC12_IC17                   1.0942124                0.275332092
## IC13_IC14 IC13_IC14                  -1.8336087                0.068372806
## IC14_IC20 IC14_IC20                   0.7908874                0.430056350
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC17               -0.1696165                  53.54754
## IC03_IC13               -0.3197982                 -74.69340
## IC04_IC12               -0.2894145                  11.74338
## IC05_IC06                0.2182136                 151.71066
## IC07_IC13                0.3961681                 -53.53290
## IC12_IC17               -0.1681853                  60.31799
## IC13_IC14                0.2647979                -225.76200
## IC14_IC20               -0.1202411                -185.59447
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC17                  72.80446                   2.484744
## IC03_IC13                 -55.43648                   1.681717
## IC04_IC12                  31.00030                   2.510147
## IC05_IC06                 170.96758                  -3.245194
## IC07_IC13                 -34.27598                  -3.277802
## IC12_IC17                  79.57491                   1.678583
## IC13_IC14                -206.50508                  -3.092962
## IC14_IC20                -166.33756                   2.828282
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC17               0.013857552              -0.3585327
## IC03_IC13               0.094320199              -0.2781578
## IC04_IC12               0.012930524              -0.3882510
## IC05_IC06               0.001394337               0.4737234
## IC07_IC13               0.001250767               0.4842942
## IC12_IC17               0.094930889              -0.2627585
## IC13_IC14               0.002290504               0.4553751
## IC14_IC20               0.005198638              -0.4122374
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC17                 10.46885                 29.88750         8.947798
## IC03_IC13               -152.75374               -133.33509         2.710811
## IC04_IC12                 35.61025                 55.02890        14.166801
## IC05_IC06                122.45496                141.87361        55.278160
## IC07_IC13               -110.07476                -90.65611       124.496029
## IC12_IC17                 37.16924                 56.58789         2.638261
## IC13_IC14               -212.73468               -193.31602        52.718034
## IC14_IC20               -207.00345               -187.58480        13.203380
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC17                  0.5745338               0.566357330
## IC03_IC13                  2.9108858               0.004081391
## IC04_IC12                  1.6609264               0.098549997
## IC05_IC06                  0.3323600               0.740022157
## IC07_IC13                 -1.4093308               0.160542822
## IC12_IC17                  2.0739640               0.039572574
## IC13_IC14                 -2.5201979               0.012639440
## IC14_IC20                  1.8422945               0.067153762
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC17             -0.08592597                 83.88668
## IC03_IC13             -0.44202806               -110.41566
## IC04_IC12             -0.27042699                 35.65420
## IC05_IC06             -0.04922147                122.55064
## IC07_IC13              0.19549205                -49.95789
## IC12_IC17             -0.32713065                 63.22810
## IC13_IC14              0.39608797               -201.79719
## IC14_IC20             -0.29742196               -209.45733
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC17                102.90959                 2.8165131
## IC03_IC13                -91.39276                 2.9764795
## IC04_IC12                 54.67710                 0.7078691
## IC05_IC06                141.57355                -2.4150774
## IC07_IC13                -30.93499                -3.0735101
## IC12_IC17                 82.25101                 3.0003029
## IC13_IC14               -182.77429                -1.4592095
## IC14_IC20               -190.43443                 1.3235037
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC17              0.005415889             -0.4036258
## IC03_IC13              0.003331189             -0.4519289
## IC04_IC12              0.479973687             -0.1081443
## IC05_IC06              0.016767411              0.3688586
## IC07_IC13              0.002456547              0.4957857
## IC12_IC17              0.003093274             -0.4294027
## IC13_IC14              0.146310369              0.2508351
## IC14_IC20              0.187404268             -0.1787898
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC17               32.477196                51.56790      10.0904147
## IC03_IC13             -135.660751              -116.57005      53.8616685
## IC04_IC12               39.816621                58.90732       0.7189301
## IC05_IC06              118.699433               137.79013       1.9417081
## IC07_IC13              -80.749737               -61.65904      36.5368325
## IC12_IC17                8.067035                27.15774      47.1011394
## IC13_IC14             -197.526971              -178.43627       1.5288431
## IC14_IC20             -265.976212              -246.88551       1.5745783
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC17                         0.32503496                         0.7457302
## IC03_IC13                        -0.52325690                         0.6017767
## IC04_IC12                        -0.08604668                         0.9315751
## IC05_IC06                        -1.56088713                         0.1212284
## IC07_IC13                        -0.86791084                         0.3872048
## IC12_IC17                        -0.86107252                         0.3909444
## IC13_IC14                         0.76137705                         0.4479508
## IC14_IC20                        -0.74009670                         0.4607113
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC17                    -0.064703223                         87.17469
## IC03_IC13                     0.077583422                        -39.54237
## IC04_IC12                     0.001098146                         65.89069
## IC05_IC06                     0.271559564                        107.49471
## IC07_IC13                     0.194510437                        -18.70223
## IC12_IC17                     0.161252814                         56.10897
## IC13_IC14                    -0.164650852                       -164.04131
## IC14_IC20                     0.137933498                        -86.35061
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC17                       103.949435                        -0.4064373
## IC03_IC13                       -22.767626                        -1.1193206
## IC04_IC12                        82.665438                         1.4889595
## IC05_IC06                       124.269452                        -0.4394489
## IC07_IC13                        -1.927483                         0.1752804
## IC12_IC17                        72.883713                        -0.8136384
## IC13_IC14                      -147.266566                        -0.9481650
## IC14_IC20                       -69.575865                         1.2629225
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC17                        0.6851276                     0.06560365
## IC03_IC13                        0.2651845                     0.17729569
## IC04_IC12                        0.1390576                    -0.26653269
## IC05_IC06                        0.6611074                     0.09833275
## IC07_IC13                        0.8611475                    -0.03507937
## IC12_IC17                        0.4174244                     0.13005861
## IC13_IC14                        0.3449046                     0.21466746
## IC14_IC20                        0.2090059                    -0.25099662
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC17                        36.04966                       53.067348
## IC03_IC13                       -74.43356                      -57.415871
## IC04_IC12                        46.61123                       63.628918
## IC05_IC06                       108.56816                      125.585852
## IC07_IC13                       -21.95994                       -4.942246
## IC12_IC17                        45.60554                       62.623236
## IC13_IC14                      -147.40800                     -130.390312
## IC14_IC20                      -111.94447                      -94.926782
##           SCequalRRB_vs_SCoverRRB.repBF
## IC01_IC17                     0.6659977
## IC03_IC13                     1.2055291
## IC04_IC12                     1.1518490
## IC05_IC06                     0.5579912
## IC07_IC13                     0.5401290
## IC12_IC17                     0.9734609
## IC13_IC14                     0.5297640
## IC14_IC20                     0.5750721
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC01_IC17 IC01_IC17         8.947798      10.0904147
## IC03_IC13 IC03_IC13         2.710811      53.8616685
## IC04_IC12 IC04_IC12        14.166801       0.7189301
## IC05_IC06 IC05_IC06        55.278160       1.9417081
## IC07_IC13 IC07_IC13       124.496029      36.5368325
## IC12_IC17 IC12_IC17         2.638261      47.1011394
## IC13_IC14 IC13_IC14        52.718034       1.5288431
## IC14_IC20 IC14_IC20        13.203380       1.5745783
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 0.9

# Z threshold
z_thresh = 0.9

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC","SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval","SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC","SCequalRRB_vs_SCoverRRB.repBF")
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")
    
    #--------------------------------------------------------------------------
    # Discovery

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)
  
    #--------------------------------------------------------------------------
    # compute replication Bayes Factors
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC03_IC12 IC03_IC12                   2.9500548               0.0035833145
## IC03_IC13 IC03_IC13                   2.1886429               0.0298617971
## IC04_IC12 IC04_IC12                   1.7055493               0.0897524456
## IC05_IC06 IC05_IC06                  -1.1831531               0.2382504425
## IC07_IC13 IC07_IC13                  -2.6225681               0.0094464192
## IC12_IC17 IC12_IC17                   0.9793108               0.3286916675
## IC13_IC14 IC13_IC14                  -2.2274790               0.0271077812
## IC17_IC18 IC17_IC18                   3.4313864               0.0007391005
## IC18_IC19 IC18_IC19                  -0.3978081               0.6912254051
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC03_IC12              -0.41989167                  26.32983
## IC03_IC13              -0.32159384                 -81.27676
## IC04_IC12              -0.25519631                  16.34733
## IC05_IC06               0.17081776                 154.99511
## IC07_IC13               0.36367254                 -59.83515
## IC12_IC17              -0.14372368                  60.37815
## IC13_IC14               0.30807244                -242.35592
## IC17_IC18              -0.49179659                  54.27725
## IC18_IC19               0.07086403                 -67.89787
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC03_IC12                  45.84347                   2.467422
## IC03_IC13                 -61.76312                   2.118814
## IC04_IC12                  35.86097                   2.827196
## IC05_IC06                 174.50875                  -3.314643
## IC07_IC13                 -40.32151                  -3.757203
## IC12_IC17                  79.89179                   2.102288
## IC13_IC14                -222.84228                  -3.486615
## IC17_IC18                  73.79089                   2.776003
## IC18_IC19                 -48.38423                   2.274385
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC03_IC12              0.0144800741              -0.3557387
## IC03_IC13              0.0353850843              -0.3285241
## IC04_IC12              0.0051907316              -0.4172130
## IC05_IC06              0.0010959585               0.4690847
## IC07_IC13              0.0002274984               0.5359627
## IC12_IC17              0.0368250095              -0.3062489
## IC13_IC14              0.0006056142               0.4896397
## IC17_IC18              0.0060450096              -0.3973893
## IC18_IC19              0.0240428334              -0.3196829
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC03_IC12                 30.74456                50.443779        13.143527
## IC03_IC13               -164.94723              -145.248008         6.453284
## IC04_IC12                 30.94980                50.649019        26.837898
## IC05_IC06                124.99396               144.693185        51.333840
## IC07_IC13               -114.61304               -94.913817       494.617934
## IC12_IC17                 36.36942                56.068644         4.663503
## IC13_IC14               -229.38628              -209.687058       183.316277
## IC17_IC18                 35.54869                55.247908        27.231818
## IC18_IC19                -13.06937                 6.629851         1.572227
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC03_IC12                  1.5486383               0.123384585
## IC03_IC13                  3.0124184               0.003000278
## IC04_IC12                  1.8739535               0.062704826
## IC05_IC06                  0.1792632               0.857950954
## IC07_IC13                 -1.5946374               0.112707022
## IC12_IC17                  2.3077226               0.022256419
## IC13_IC14                 -2.1807901               0.030614638
## IC17_IC18                  2.5468056               0.011785942
## IC18_IC19                  1.5264319               0.128816934
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC03_IC12             -0.25456546                 58.61301
## IC03_IC13             -0.48229966               -103.90343
## IC04_IC12             -0.32726059                 30.19700
## IC05_IC06             -0.02874286                120.77866
## IC07_IC13              0.23465455                -42.99462
## IC12_IC17             -0.39147562                 61.15969
## IC13_IC14              0.36512361               -187.92639
## IC17_IC18             -0.41784998                 56.10796
## IC18_IC19             -0.26790057                -86.29745
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC03_IC12                 77.39240                 1.4917369
## IC03_IC13                -85.12404                 2.6324914
## IC04_IC12                 48.97639                 0.2360359
## IC05_IC06                139.55806                -2.2560310
## IC07_IC13                -24.21523                -2.4630636
## IC12_IC17                 79.93908                 2.5253906
## IC13_IC14               -169.14700                -0.8457121
## IC17_IC18                 74.88735                 1.4035518
## IC18_IC19                -67.51806                 2.4132771
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC03_IC12              0.137666087             -0.2346498
## IC03_IC13              0.009275937             -0.4077152
## IC04_IC12              0.813695759             -0.0456331
## IC05_IC06              0.025374147              0.3696337
## IC07_IC13              0.014796084              0.4171082
## IC12_IC17              0.012494058             -0.3786487
## IC13_IC14              0.398930761              0.1767343
## IC17_IC18              0.162320244             -0.2047941
## IC18_IC19              0.016898289             -0.3952378
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC03_IC12               12.924087                31.73888       2.1396824
## IC03_IC13             -125.688943              -106.87415      20.5529595
## IC04_IC12               42.286263                61.10105       0.3724043
## IC05_IC06              115.648680               134.46347       2.0120331
## IC07_IC13              -77.174491               -58.35970      11.6745316
## IC12_IC17                8.817891                27.63268      16.1654706
## IC13_IC14             -185.519379              -166.70459       0.6475302
## IC17_IC18               38.304652                57.11944       1.3624388
## IC18_IC19              -52.317823               -33.50303      10.3020900
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC03_IC12                          0.7483015                        0.45575471
## IC03_IC13                         -0.7283203                        0.46784831
## IC04_IC12                         -0.5205385                        0.60365544
## IC05_IC06                         -1.0809649                        0.28189853
## IC07_IC13                         -0.5515502                        0.58229075
## IC12_IC17                         -1.4359984                        0.15362636
## IC13_IC14                          0.5512722                        0.58248069
## IC17_IC18                          0.3923431                        0.69550659
## IC18_IC19                         -1.6853271                        0.09454611
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC03_IC12                     -0.13283149                         48.07414
## IC03_IC13                      0.11417045                        -39.86271
## IC04_IC12                      0.07251492                         65.23453
## IC05_IC06                      0.20206808                        108.87030
## IC07_IC13                      0.11976008                        -18.07354
## IC12_IC17                      0.25097355                         53.94688
## IC13_IC14                     -0.08989251                       -166.37880
## IC17_IC18                     -0.08380033                          9.88557
## IC18_IC19                      0.29376949                        -10.82918
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC03_IC12                        64.898268                         0.7078715
## IC03_IC13                       -23.038586                        -0.6125566
## IC04_IC12                        82.058652                         1.9526977
## IC05_IC06                       125.694429                        -0.4304303
## IC07_IC13                        -1.249410                        -0.4736645
## IC12_IC17                        70.771011                        -0.3381107
## IC13_IC14                      -149.554671                        -1.3752581
## IC17_IC18                        26.709696                         1.0116727
## IC18_IC19                         5.994947                        -0.1755206
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC03_IC12                       0.48035346                    -0.11954417
## IC03_IC13                       0.54129130                     0.09230374
## IC04_IC12                       0.05310846                    -0.35214621
## IC05_IC06                       0.66762985                     0.09133783
## IC07_IC13                       0.63657203                     0.08676581
## IC12_IC17                       0.73585154                     0.04183277
## IC13_IC14                       0.17153098                     0.32756894
## IC17_IC18                       0.31366459                    -0.18365873
## IC18_IC19                       0.86095691                     0.02214878
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC03_IC12                        63.42217                       80.487292
## IC03_IC13                       -75.51437                      -58.449244
## IC04_IC12                        45.36737                       62.432493
## IC05_IC06                       108.33571                      125.400830
## IC07_IC13                       -23.56616                       -6.501035
## IC12_IC17                        45.17502                       62.240144
## IC13_IC14                      -150.72817                     -133.663049
## IC17_IC18                        43.86876                       60.933887
## IC18_IC19                        37.79721                       54.862332
##           SCequalRRB_vs_SCoverRRB.repBF
## IC03_IC12                     0.9030264
## IC03_IC13                     0.8441854
## IC04_IC12                     1.0275350
## IC05_IC06                     0.6893732
## IC07_IC13                     0.7842290
## IC12_IC17                     0.5459025
## IC13_IC14                     0.7187953
## IC17_IC18                     1.0674748
## IC18_IC19                     0.3993259
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF
## IC03_IC12 IC03_IC12        13.143527       2.1396824
## IC03_IC13 IC03_IC13         6.453284      20.5529595
## IC04_IC12 IC04_IC12        26.837898       0.3724043
## IC05_IC06 IC05_IC06        51.333840       2.0120331
## IC07_IC13 IC07_IC13       494.617934      11.6745316
## IC12_IC17 IC12_IC17         4.663503      16.1654706
## IC13_IC14 IC13_IC14       183.316277       0.6475302
## IC17_IC18 IC17_IC18        27.231818       1.3624388
## IC18_IC19 IC18_IC19         1.572227      10.3020900
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

Main analysis - Z = 1

# Z threshold
z_thresh = 1

fname = sprintf("partialCorData_ridge_lambda1.diffzscoreGrps_z%s.txt",as.character(z_thresh))

fname2open = file.path(datapath, fname)
df = read.delim(fname2open)
df = subset(df,df$subgrp!="RRB_over_SC")

tmp_df = read.csv(file.path(datapath,sprintf("tidy_euaims_NDAR_subtypes_diffscore_z%s.csv",as.character(z_thresh))))
tmp_df = subset(tmp_df,tmp_df$svm_pred_labels!="RRB_over_SC")
tmp_df$A_pct_severity = (tmp_df$A1_pct_severity+tmp_df$A2_pct_severity+tmp_df$A3_pct_severity)/3
tmp_df$B_pct_severity = (tmp_df$B1_pct_severity+tmp_df$B2_pct_severity+tmp_df$B3_pct_severity+tmp_df$B4_pct_severity)/4
tmp_df$AB_pct_severity = tmp_df$A_pct_severity + tmp_df$B_pct_severity 

asd_df = merge(tmp_df[,c("subid","A1_pct_severity","A2_pct_severity","A3_pct_severity",
                        "B1_pct_severity","B2_pct_severity","B3_pct_severity","B4_pct_severity",
                        "A_pct_severity","B_pct_severity","AB_pct_severity","z_ds")],
           df,
           by="subid")

vine_df = read.csv(here("asd_subgrp_data_rsfmri_ALL_DSM5_diffzscoreGrps_z1.csv"))

asd_df = merge(asd_df, vine_df[,c("subid","vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard")], by = "subid")

#------------------------------------------------------------------------------
# Main analysis
RUNANALYSIS = TRUE

if (RUNANALYSIS==TRUE) {
  
  # columns with connectivity data
  vars2use = colnames(df)[10:ncol(df)]
  
  cnames = c("compNames",
             "SCequalRRB_Disc_vs_TD.tstat","SCequalRRB_Disc_vs_TD.pval", 
             "SCequalRRB_Disc_vs_TD.es","SCequalRRB_Disc_vs_TD.AIC","SCequalRRB_Disc_vs_TD.BIC",
             "SCequalRRB_Rep_vs_TD.tstat","SCequalRRB_Rep_vs_TD.pval","SCequalRRB_Rep_vs_TD.es", 
             "SCequalRRB_Rep_vs_TD.AIC","SCequalRRB_Rep_vs_TD.BIC", "SCequalRRB.repBF",
             "SCoverRRB_Disc_vs_TD.tstat","SCoverRRB_Disc_vs_TD.pval", 
             "SCoverRRB_Disc_vs_TD.es","SCoverRRB_Disc_vs_TD.AIC","SCoverRRB_Disc_vs_TD.BIC",
             "SCoverRRB_Rep_vs_TD.tstat","SCoverRRB_Rep_vs_TD.pval","SCoverRRB_Rep_vs_TD.es",
             "SCoverRRB_Rep_vs_TD.AIC","SCoverRRB_Rep_vs_TD.BIC", "SCoverRRB.repBF",
             "SCequalRRB_Disc_vs_SCoverRRB.tstat","SCequalRRB_Disc_vs_SCoverRRB.pval",
             "SCequalRRB_Disc_vs_SCoverRRB.es","SCequalRRB_Disc_vs_SCoverRRB.AIC",
             "SCequalRRB_Disc_vs_SCoverRRB.BIC",
             "SCequalRRB_Rep_vs_SCoverRRB.tstat","SCequalRRB_Rep_vs_SCoverRRB.pval",
             "SCequalRRB_Rep_vs_SCoverRRB.es",
             "SCequalRRB_Rep_vs_SCoverRRB.AIC","SCequalRRB_Rep_vs_SCoverRRB.BIC",
             "SCequalRRB_vs_SCoverRRB.repBF",
             "SCcorr_Disc.r","SCcorr_Disc.t","SCcorr_Disc.pval",
             "SCcorr_Rep.r","SCcorr_Rep.t","SCcorr_Rep.pval","SCcorr.repBF",
             "RRBcorr_Disc.r","RRBcorr_Disc.t","RRBcorr_Disc.pval",
             "RRBcorr_Rep.r","RRBcorr_Rep.t","RRBcorr_Rep.pval","RRBcorr.repBF",
             "SumSCRRB_Disc.r","SumSCRRB_Disc.t","SumSCRRB_Disc.pval",
             "SumSCRRB_Rep.r","SumSCRRB_Rep.t","SumSCRRB_Rep.pval","SumSCRRB.repBF",
             "zds_Disc.r","zds_Disc.t","zds_Disc.pval",
             "zds_Rep.r","zds_Rep.t","zds_Rep.pval","zds.repBF",
             "SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Disc.t","SumSCRRB_SCequalRRB_Disc.pval",
             "SumSCRRB_SCequalRRB_Rep.r","SumSCRRB_SCequalRRB_Rep.t",
             "SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
             "zds_SCequalRRB_Disc.r","zds_SCequalRRB_Disc.t","zds_SCequalRRB_Disc.pval",
             "zds_SCequalRRB_Rep.r","zds_SCequalRRB_Rep.t","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
             "zds_SCoverRRB_Disc.r","zds_SCoverRRB_Disc.t","zds_SCoverRRB_Disc.pval",
             "zds_SCoverRRB_Rep.r","zds_SCoverRRB_Rep.t","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
             "VinelandABC_Disc.r","VinelandABC_Disc.t","VinelandABC_Disc.pval",
             "VinelandABC_Rep.r","VinelandABC_Rep.t",
             "VinelandABC_Rep.pval","VinelandABC.repBF",
             "VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Disc.t","VinelandABC_SCequalRRB_Disc.pval",
             "VinelandABC_SCequalRRB_Rep.r","VinelandABC_SCequalRRB_Rep.t",
             "VinelandABC_SCequalRRB_Rep.pval","VinelandABC_SCequalRRB.repBF",
             "VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Disc.t","VinelandABC_SCoverRRB_Disc.pval",
             "VinelandABC_SCoverRRB_Rep.r","VinelandABC_SCoverRRB_Rep.t",
             "VinelandABC_SCoverRRB_Rep.pval","VinelandABC_SCoverRRB.repBF")
  
  
  # "vabsdscoresc_dss","vabsdscoresd_dss","vabsdscoress_dss","vabsabcabc_standard"
  
  aovres = data.frame(matrix(nrow = length(vars2use),ncol = length(cnames)))
  colnames(aovres) = cnames
  rownames(aovres) = vars2use
  aovres$compNames = vars2use
  vars2loop = c(1:length(vars2use))

  for (i in vars2loop) {
    y_var = vars2use[i]

    # run analyses on Discovery and Replication datasets
    df_Disc = subset(df, df$dataset=="Discovery")
    df_Rep = subset(df, df$dataset=="Replication")

    #--------------------------------------------------------------------------
    # Discovery
    
    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Disc,
                                  na.action = na.omit)))
    df_Disc$data2plot = resid(mod2use)
  
    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD2 = subset(df_Disc, df_Disc$subgrp=="SC_over_RRB" | df_Disc$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Disc_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Disc_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Disc.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Disc.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Disc, df_Disc$subgrp=="RRB_over_SC" | df_Disc$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Disc_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Disc_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Disc.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Disc.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Disc, df_Disc$subgrp=="SC_equal_RRB" | df_Disc$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Disc_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Disc.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Disc.BIC = BIC(mod2use)
    
    aovres[y_var,"SCequalRRB_Disc_vs_TD.tstat"] = SCequalRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_TD.pval"] = SCequalRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Disc_vs_TD.AIC"] = SCequalRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_TD.BIC"] = SCequalRRB_vs_TD_Disc.BIC

    aovres[y_var,"SCoverRRB_Disc_vs_TD.tstat"] = SCoverRRB_vs_TD_Disc_statistic
    aovres[y_var,"SCoverRRB_Disc_vs_TD.pval"] = SCoverRRB_vs_TD_Disc_p.value
    aovres[y_var,"SCoverRRB_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Disc_vs_TD.AIC"] = SCoverRRB_vs_TD_Disc.AIC
    aovres[y_var,"SCoverRRB_Disc_vs_TD.BIC"] = SCoverRRB_vs_TD_Disc.BIC

    # aovres[y_var,"RRBoverSC_Disc_vs_TD.tstat"] = RRBoverSC_vs_TD_Disc_statistic
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.pval"] = RRBoverSC_vs_TD_Disc_p.value
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="RRB_over_SC"],
    #                                          df_Disc$data2plot[df_Disc$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.AIC"] = RRBoverSC_vs_TD_Disc.AIC
    # aovres[y_var,"RRBoverSC_Disc_vs_TD.BIC"] = RRBoverSC_vs_TD_Disc.BIC
    
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Disc_statistic
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Disc_p.value
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.es"] = cohens_d(df_Disc$data2plot[df_Disc$subgrp=="SC_equal_RRB"],
                                             df_Disc$data2plot[df_Disc$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Disc.AIC
    aovres[y_var,"SCequalRRB_Disc_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Disc.BIC

    
    #--------------------------------------------------------------------------
    DASD = subset(asd_df, asd_df$dataset=="Discovery")
    DASD$site = factor(DASD$site)
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SCcorr_Disc.t"] = res$tTable["A_pct_severity","t-value"]
    aovres[y_var,"SCcorr_Disc.pval"] = res$tTable["A_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$A_pct_severity)
    aovres[y_var,"SCcorr_Disc.r"] = res$estimate

    
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"RRBcorr_Disc.t"] = res$tTable["B_pct_severity","t-value"]
    aovres[y_var,"RRBcorr_Disc.pval"] = res$tTable["B_pct_severity","p-value"]

    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$B_pct_severity)
    aovres[y_var,"RRBcorr_Disc.r"] = res$estimate

    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$AB_pct_severity)
    aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate

    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_Disc.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_Disc.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$z_ds)
    aovres[y_var,"zds_Disc.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_Disc.r"] = res$estimate
    
    
    
    DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
    DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")

    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCequalRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCequalRRB_Disc.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCoverRRB_Disc.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCoverRRB_Disc.r"] = res$estimate
    
    

    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_SCequalRRB_Disc.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
    aovres[y_var,"SumSCRRB_SCequalRRB_Disc.r"] = res$estimate
    
    
            
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCequalRRB_Disc.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCequalRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
    aovres[y_var,"zds_SCequalRRB_Disc.r"] = res$estimate
    
  
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCoverRRB_Disc.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCoverRRB_Disc.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
    aovres[y_var,"zds_SCoverRRB_Disc.r"] = res$estimate
      
  #     res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SCcorr_Disc.r"] = res$estimate
  #   aovres[y_var,"SCcorr_Disc.pval"] = res$p.value
  #     res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"RRBcorr_Disc.r"] = res$estimate
  #   aovres[y_var,"RRBcorr_Disc.pval"] = res$p.value
  #     res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SumSCRRB_Disc.r"] = res$estimate
  #   aovres[y_var,"SumSCRRB_Disc.pval"] = res$p.value
    n_orig = dim(DASD)[1]
    #--------------------------------------------------------------------------
    
    
    #--------------------------------------------------------------------------
    # Replication

    # grab residuals after accounting for sex and scan_age
    fx_form2 = as.formula(sprintf("%s ~ %s + %s",y_var,"sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = df_Rep,
                                  na.action = na.omit)))
    df_Rep$data2plot = resid(mod2use)

    # compute t-stats
    fx_form = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"subgrp","sex","scan_age"))
    rx_form = as.formula(sprintf("~ 1|%s","site"))
    
    DASD1 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD1, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_TD_Rep.BIC = BIC(mod2use)

    DASD2 = subset(df_Rep, df_Rep$subgrp=="SC_over_RRB" | df_Rep$subgrp=="TD")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD2, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCoverRRB_vs_TD_Rep_statistic = res$tTable[2,4]
    SCoverRRB_vs_TD_Rep_p.value = res$tTable[2,5]
    SCoverRRB_vs_TD_Rep.AIC = AIC(mod2use)
    SCoverRRB_vs_TD_Rep.BIC = BIC(mod2use)

  #     DASD3 = subset(df_Rep, df_Rep$subgrp=="RRB_over_SC" | df_Rep$subgrp=="TD")
  #     mod2use = eval(substitute(lme(fixed = fx_form, 
  #                                   random = rx_form, 
  #                                   data = DASD3, 
  #                                   na.action = na.omit)))
  #   res = summary(mod2use)
  #   RRBoverSC_vs_TD_Rep_statistic = res$tTable[2,4]
  #   RRBoverSC_vs_TD_Rep_p.value = res$tTable[2,5]
  #   RRBoverSC_vs_TD_Rep.AIC = AIC(mod2use)
  #   RRBoverSC_vs_TD_Rep.BIC = BIC(mod2use)
    
    DASD4 = subset(df_Rep, df_Rep$subgrp=="SC_equal_RRB" | df_Rep$subgrp=="SC_over_RRB")
    mod2use = eval(substitute(lme(fixed = fx_form, 
                                  random = rx_form, 
                                  data = DASD4, 
                                  na.action = na.omit)))
    res = summary(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep_statistic = res$tTable[2,4]
    SCequalRRB_vs_SCoverRRB_Rep_p.value = res$tTable[2,5]
    SCequalRRB_vs_SCoverRRB_Rep.AIC = AIC(mod2use)
    SCequalRRB_vs_SCoverRRB_Rep.BIC = BIC(mod2use)

    aovres[y_var,"SCequalRRB_Rep_vs_TD.tstat"] = SCequalRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_TD.pval"] = SCequalRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCequalRRB_Rep_vs_TD.AIC"] = SCequalRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_TD.BIC"] = SCequalRRB_vs_TD_Rep.BIC

    aovres[y_var,"SCoverRRB_Rep_vs_TD.tstat"] = SCoverRRB_vs_TD_Rep_statistic
    aovres[y_var,"SCoverRRB_Rep_vs_TD.pval"] = SCoverRRB_vs_TD_Rep_p.value
    aovres[y_var,"SCoverRRB_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"],
                                            df_Rep$data2plot[df_Rep$subgrp=="TD"])
    aovres[y_var,"SCoverRRB_Rep_vs_TD.AIC"] = SCoverRRB_vs_TD_Rep.AIC
    aovres[y_var,"SCoverRRB_Rep_vs_TD.BIC"] = SCoverRRB_vs_TD_Rep.BIC

    # aovres[y_var,"RRBoverSC_Rep_vs_TD.tstat"] = RRBoverSC_vs_TD_Rep_statistic
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.pval"] = RRBoverSC_vs_TD_Rep_p.value
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="RRB_over_SC"],
    #                                         df_Rep$data2plot[df_Rep$subgrp=="TD"])
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.AIC"] = RRBoverSC_vs_TD_Rep.AIC
    # aovres[y_var,"RRBoverSC_Rep_vs_TD.BIC"] = RRBoverSC_vs_TD_Rep.BIC
    
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.tstat"] = SCequalRRB_vs_SCoverRRB_Rep_statistic
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.pval"] = SCequalRRB_vs_SCoverRRB_Rep_p.value
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.es"] = cohens_d(df_Rep$data2plot[df_Rep$subgrp=="SC_equal_RRB"],
                                             df_Rep$data2plot[df_Rep$subgrp=="SC_over_RRB"])
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.AIC"] = SCequalRRB_vs_SCoverRRB_Rep.AIC
    aovres[y_var,"SCequalRRB_Rep_vs_SCoverRRB.BIC"] = SCequalRRB_vs_SCoverRRB_Rep.BIC
    
    
    #--------------------------------------------------------------------------
    DASD = subset(asd_df, asd_df$dataset=="Replication")
    DASD$site = factor(DASD$site)
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"A_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SCcorr_Rep.t"] = res$tTable["A_pct_severity","t-value"]
    aovres[y_var,"SCcorr_Rep.pval"] = res$tTable["A_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"A_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ A_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$A_pct_severity)
    aovres[y_var,"SCcorr_Rep.r"] = res$estimate

    
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"B_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"RRBcorr_Rep.t"] = res$tTable["B_pct_severity","t-value"]
    aovres[y_var,"RRBcorr_Rep.pval"] = res$tTable["B_pct_severity","p-value"]

    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"B_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ B_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$B_pct_severity)
    aovres[y_var,"RRBcorr_Rep.r"] = res$estimate

    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$AB_pct_severity)
    aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
    
    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_Rep.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_Rep.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$z_ds)
    aovres[y_var,"zds_Rep.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD$covadj = as.numeric(t(DASD[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD$covadj,DASD$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_Rep.r"] = res$estimate
    
    
    DASD_SCequalRRB = subset(DASD,DASD$subgrp=="SC_equal_RRB")
    DASD_SCoverRRB = subset(DASD,DASD$subgrp=="SC_over_RRB")

    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCequalRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCequalRRB_Rep.r"] = res$estimate
    
    
    # Vineland ABC
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"] = res$tTable["vabsabcabc_standard","t-value"]
    aovres[y_var,"VinelandABC_SCoverRRB_Rep.pval"] = res$tTable["vabsabcabc_standard","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"vabsabcabc_standard","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ vabsabcabc_standard + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$vabsabcabc_standard)
    aovres[y_var,"VinelandABC_SCoverRRB_Rep.r"] = res$estimate    
    
    
    

    
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"] = res$tTable["AB_pct_severity","t-value"]
    aovres[y_var,"SumSCRRB_SCequalRRB_Rep.pval"] = res$tTable["AB_pct_severity","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"AB_pct_severity","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ AB_pct_severity + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$AB_pct_severity)
    aovres[y_var,"SumSCRRB_SCequalRRB_Rep.r"] = res$estimate

            
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCequalRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCequalRRB_Rep.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCequalRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCequalRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCequalRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCequalRRB$covadj = as.numeric(t(DASD_SCequalRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCequalRRB$covadj,DASD_SCequalRRB$z_ds)
    aovres[y_var,"zds_SCequalRRB_Rep.r"] = res$estimate
    
  
    fx_form2 = as.formula(sprintf("%s ~ %s + %s + %s",y_var,"z_ds","sex","scan_age"))
    mod2use = eval(substitute(lme(fixed = fx_form2,
                                  random = rx_form,
                                  data = DASD_SCoverRRB,
                                  na.action = na.omit)))
    res = summary(mod2use)
    aovres[y_var,"zds_SCoverRRB_Rep.t"] = res$tTable["z_ds","t-value"]
    aovres[y_var,"zds_SCoverRRB_Rep.pval"] = res$tTable["z_ds","p-value"]
    
    lm_form = as.formula(sprintf("%s ~ %s + %s + %s + %s",y_var,"z_ds","sex","scan_age","site"))
    lm_mod = lm(formula=lm_form, data = DASD_SCoverRRB, na.action = na.omit)
        covname2use = c("sexMale","scan_age", "sitekcl","sitenijmegen","siteutrecht")
    beta1 = lm_mod$coefficients[covname2use, drop = FALSE]
    beta1[is.na(beta1)] = 0
    full_model = model.matrix(~0+ z_ds + sex + scan_age + site, data=DASD_SCoverRRB)
    covname2use = c("sexMale","scan_age","sitekcl","sitenijmegen","siteutrecht")
    DASD_SCoverRRB$covadj = as.numeric(t(DASD_SCoverRRB[,y_var] - beta1 %*% t(full_model[,covname2use])))
    res = cor.test(DASD_SCoverRRB$covadj,DASD_SCoverRRB$z_ds)
    aovres[y_var,"zds_SCoverRRB_Rep.r"] = res$estimate
      
    
  #     res = cor.test(DASD[,"A_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SCcorr_Rep.r"] = res$estimate
  #   aovres[y_var,"SCcorr_Rep.pval"] = res$p.value
  #     res = cor.test(DASD[,"B_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"RRBcorr_Rep.r"] = res$estimate
  #   aovres[y_var,"RRBcorr_Rep.pval"] = res$p.value
  #     res = cor.test(DASD[,"AB_pct_severity"],DASD[,y_var])
  #   aovres[y_var,"SumSCRRB_Rep.r"] = res$estimate
  #   aovres[y_var,"SumSCRRB_Rep.pval"] = res$p.value
    n_rep = dim(DASD)[1]
    #--------------------------------------------------------------------------

    current_state = sprintf("Loop %d",i)
    fname2save = file.path(resultpath,"anova_allconnections","monitor.csv")
    write.table(current_state, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

    #--------------------------------------------------------------------------
    # compute replication Bayes Factors  
    res_bf = BFSALL(tobs = SCequalRRB_vs_TD_Disc_statistic, 
                      trep = SCequalRRB_vs_TD_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="TD"), 
                      m2 = sum(df_Rep$subgrp=="TD"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB.repBF"] = res_bf[4,2]
  
    res_bf = BFSALL(tobs = SCoverRRB_vs_TD_Disc_statistic, 
                    trep = SCoverRRB_vs_TD_Rep_statistic,
                    n1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                    n2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                    m1 = sum(df_Disc$subgrp=="TD"), 
                    m2 = sum(df_Rep$subgrp=="TD"),
                    sample = 2, 
                    Type = 'ALL')
    aovres[y_var,"SCoverRRB.repBF"] = res_bf[4,2]
  
    # # print("RRBoverSC")
    # res_bf = BFSALL(tobs = RRBoverSC_vs_TD_Disc_statistic, 
    #                 trep = RRBoverSC_vs_TD_Rep_statistic, 
    #                 n1 = sum(df_Disc$subgrp=="RRB_over_SC"), 
    #                 n2 = sum(df_Rep$subgrp=="RRB_over_SC"),
    #                 m1 = sum(df_Disc$subgrp=="TD"), 
    #                 m2 = sum(df_Rep$subgrp=="TD"),
    #                 sample = 2,
    #                 Type = 'ALL')
    # aovres[y_var,"RRBoverSC.repBF"] = res_bf[4,2]
    
    res_bf = BFSALL(tobs = SCequalRRB_vs_SCoverRRB_Disc_statistic, 
                      trep = SCequalRRB_vs_SCoverRRB_Rep_statistic, 
                      n1 = sum(df_Disc$subgrp=="SC_equal_RRB"), 
                      n2 = sum(df_Rep$subgrp=="SC_equal_RRB"),
                      m1 = sum(df_Disc$subgrp=="SC_over_RRB"), 
                      m2 = sum(df_Rep$subgrp=="SC_over_RRB"),
                      sample = 2, 
                      Type = 'ALL')
    aovres[y_var,"SCequalRRB_vs_SCoverRRB.repBF"] = res_bf[4,2]
    
    #--------------------------------------------------------------------------
    res_bf = BFSALL(tobs =aovres[y_var,"SCcorr_Disc.t"], 
                      trep = aovres[y_var,"SCcorr_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"SCcorr.repBF"] = res_bf["Replication BF","Replication 1"]
    
    res_bf = BFSALL(tobs =aovres[y_var,"RRBcorr_Disc.t"], 
                      trep = aovres[y_var,"RRBcorr_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"RRBcorr.repBF"] = res_bf["Replication BF","Replication 1"]

    res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_Disc.t"], 
                      trep = aovres[y_var,"SumSCRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"SumSCRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    res_bf = BFSALL(tobs =aovres[y_var,"zds_Disc.t"], 
                      trep = aovres[y_var,"zds_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"zds.repBF"] = res_bf["Replication BF","Replication 1"]

    
    
    res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_Disc.t"], 
                      trep = aovres[y_var,"VinelandABC_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"VinelandABC.repBF"] = res_bf["Replication BF","Replication 1"]

    
    res_bf = BFSALL(tobs =aovres[y_var,"SumSCRRB_SCequalRRB_Disc.t"], 
                      trep = aovres[y_var,"SumSCRRB_SCequalRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"SumSCRRB_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    res_bf = BFSALL(tobs =aovres[y_var,"zds_SCequalRRB_Disc.t"], 
                      trep = aovres[y_var,"zds_SCequalRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"zds_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    
    res_bf = BFSALL(tobs =aovres[y_var,"zds_SCoverRRB_Disc.t"], 
                      trep = aovres[y_var,"zds_SCoverRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"zds_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]
    
    
    res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCequalRRB_Disc.t"], 
                      trep = aovres[y_var,"VinelandABC_SCequalRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"VinelandABC_SCequalRRB.repBF"] = res_bf["Replication BF","Replication 1"]
    
    res_bf = BFSALL(tobs =aovres[y_var,"VinelandABC_SCoverRRB_Disc.t"], 
                      trep = aovres[y_var,"VinelandABC_SCoverRRB_Rep.t"], 
                      n1 = n_orig, 
                      n2 = n_rep,
                      sample = 1, 
                      Type = 'ALL')
    aovres[y_var,"VinelandABC_SCoverRRB.repBF"] = res_bf["Replication BF","Replication 1"]

    # res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SCcorr_Disc.r"], 
    #                                                         n.orig = n_orig, 
    #                                                         r.rep = aovres[y_var,"SCcorr_Rep.r"], 
    #                                                         n.rep = n_rep)
    # aovres[y_var,"SCcorr.repBF"] = res_bf["BF10"]
    # res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"RRBcorr_Disc.r"], 
    #                                                         n.orig = n_orig, 
    #                                                         r.rep = aovres[y_var,"RRBcorr_Rep.r"], 
    #                                                         n.rep = n_rep)
    # aovres[y_var,"RRBcorr.repBF"] = res_bf["BF10"]
    # res_bf = CorrelationReplicationBF(r.orig = aovres[y_var,"SumSCRRB_Disc.r"], 
    #                                                         n.orig = n_orig, 
    #                                                         r.rep = aovres[y_var,"SumSCRRB_Rep.r"], 
    #                                                         n.rep = n_rep)
    # aovres[y_var,"SumSCRRB.repBF"] = res_bf["BF10"]
    #--------------------------------------------------------------------------

    # save results to a file
    fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
    # write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

  }
  
  mask1 = aovres$SCequalRRB.repBF>=10
  mask2 = aovres$SCoverRRB.repBF>=10
  # mask3 = aovres$RRBoverSC.repBF>=10
  mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
  mask5 = aovres$SCcorr.repBF>=10
  mask6 = aovres$RRBcorr.repBF>=10
  mask7 = aovres$SumSCRRB.repBF>=10
  mask8 = aovres$zds.repBF>=10
  mask9 = aovres$zds_SCequalRRB.repBF>=10
  mask10 = aovres$zds_SCoverRRB.repBF>=10
  mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
  mask12 = aovres$VinelandABC.repBF>=10
  mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
  mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
  mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14
  print(aovres[mask_allBF,])
  
  # save results to a file
  fname2save = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_diffzscoreGrps_z%s.csv",as.character(z_thresh)))
  write.table(aovres, file = fname2save, sep = ",", quote = FALSE, col.names = NA)

} else {
  
  fname = file.path(resultpath,"anova_allconnections",sprintf("partialCor_ALLsubs_ridge1_lme_fx_subgrp_sex_scan_age_rx_site_z%s.xlsx",as.character(z_thresh)))
  aovres = read_excel(fname)
}
##           compNames SCequalRRB_Disc_vs_TD.tstat SCequalRRB_Disc_vs_TD.pval
## IC01_IC12 IC01_IC12                  -0.2496273               0.8031410927
## IC03_IC13 IC03_IC13                   2.2285209               0.0269997941
## IC04_IC11 IC04_IC11                  -0.1484457               0.8821460000
## IC04_IC12 IC04_IC12                   1.7940013               0.0743782660
## IC05_IC06 IC05_IC06                  -1.2919451               0.1979210508
## IC05_IC19 IC05_IC19                   0.9851454               0.3257860231
## IC07_IC13 IC07_IC13                  -2.7387981               0.0067439048
## IC07_IC16 IC07_IC16                   1.2745753               0.2039920282
## IC08_IC11 IC08_IC11                  -0.3117874               0.7555386701
## IC08_IC20 IC08_IC20                  -0.1264030               0.8995444968
## IC11_IC12 IC11_IC12                   1.2946614               0.1969838128
## IC12_IC17 IC12_IC17                   1.0821493               0.2805363825
## IC13_IC14 IC13_IC14                  -2.3577628               0.0193861300
## IC14_IC20 IC14_IC20                   1.3632092               0.1744047813
## IC15_IC17 IC15_IC17                  -1.2965093               0.1963481239
## IC17_IC18 IC17_IC18                   3.6411454               0.0003485864
##           SCequalRRB_Disc_vs_TD.es SCequalRRB_Disc_vs_TD.AIC
## IC01_IC12               0.03053444                -0.9200558
## IC03_IC13              -0.31893337               -90.0593434
## IC04_IC11               0.01274671               193.4339021
## IC04_IC12              -0.25554293                22.1627717
## IC05_IC06               0.18250921               154.8103250
## IC05_IC19              -0.13856428               136.2350990
## IC07_IC13               0.36930472               -65.6644763
## IC07_IC16              -0.18364899                74.2220213
## IC08_IC11               0.03386850               108.9647159
## IC08_IC20               0.02044076              -115.1178960
## IC11_IC12              -0.18820948                11.5947112
## IC12_IC17              -0.15667411                58.8410802
## IC13_IC14               0.31519898              -252.4141615
## IC14_IC20              -0.19509918              -211.5651943
## IC15_IC17               0.18949119                35.8945904
## IC17_IC18              -0.51483822                51.7198686
##           SCequalRRB_Disc_vs_TD.BIC SCequalRRB_Rep_vs_TD.tstat
## IC01_IC12                  18.77917                 -0.5435691
## IC03_IC13                 -70.36012                  2.4683205
## IC04_IC11                 213.13312                  0.6090771
## IC04_IC12                  41.86199                  2.5764811
## IC05_IC06                 174.50955                 -3.4648826
## IC05_IC19                 155.93432                  0.7017945
## IC07_IC13                 -45.96525                 -3.8668930
## IC07_IC16                  93.92124                 -0.8574564
## IC08_IC11                 128.66394                  0.6556000
## IC08_IC20                 -95.41867                  1.8104838
## IC11_IC12                  31.29393                  0.5271688
## IC12_IC17                  78.54030                  2.0453046
## IC13_IC14                -232.71494                 -3.7317613
## IC14_IC20                -191.86597                  2.5515062
## IC15_IC17                  55.59381                  0.1683646
## IC17_IC18                  71.41909                  2.6365540
##           SCequalRRB_Rep_vs_TD.pval SCequalRRB_Rep_vs_TD.es
## IC01_IC12              0.5873464610              0.06816497
## IC03_IC13              0.0144183974             -0.37502204
## IC04_IC11              0.5431680402             -0.08596104
## IC04_IC12              0.0107052798             -0.37221036
## IC05_IC06              0.0006496279              0.48026006
## IC05_IC19              0.4836272797             -0.09183902
## IC07_IC13              0.0001492405              0.53888946
## IC07_IC16              0.3922241929              0.12285958
## IC08_IC11              0.5128387076             -0.09439621
## IC08_IC20              0.0717291247             -0.25879566
## IC11_IC12              0.5986635397             -0.08832568
## IC12_IC17              0.0421408659             -0.29104485
## IC13_IC14              0.0002480568              0.51583745
## IC14_IC20              0.0114769542             -0.34332152
## IC15_IC17              0.8664674609             -0.02669512
## IC17_IC18              0.0090361760             -0.36819744
##           SCequalRRB_Rep_vs_TD.AIC SCequalRRB_Rep_vs_TD.BIC SCequalRRB.repBF
## IC01_IC12                -28.38384                -8.504608        0.7955174
## IC03_IC13               -170.54202              -150.662781       14.0854368
## IC04_IC11                213.64541               233.524647        0.7314191
## IC04_IC12                 29.03154                48.910775       16.1957454
## IC05_IC06                123.82345               143.702690       80.0450180
## IC05_IC19                124.85020               144.729436        0.8767820
## IC07_IC13               -118.76241               -98.883173      737.3305480
## IC07_IC16                113.58642               133.465656        0.3237813
## IC08_IC11                 74.34074                94.219976        0.6878668
## IC08_IC20                -90.43583               -70.556592        1.4262907
## IC11_IC12                -68.66126               -48.782025        0.6891131
## IC12_IC17                 35.35480                55.234039        4.5013271
## IC13_IC14               -238.33387              -218.454631      393.9080616
## IC14_IC20               -217.49223              -197.612992       12.5596603
## IC15_IC17                 82.58892               102.468155        0.4119129
## IC17_IC18                 33.64229                53.521530       16.2852363
##           SCoverRRB_Disc_vs_TD.tstat SCoverRRB_Disc_vs_TD.pval
## IC01_IC12                 -1.4989340               0.135861199
## IC03_IC13                  2.9664175               0.003475083
## IC04_IC11                 -0.2685483               0.788623270
## IC04_IC12                  1.6651903               0.097831688
## IC05_IC06                  0.3473936               0.728752209
## IC05_IC19                  0.8833145               0.378391662
## IC07_IC13                 -1.5394266               0.125675522
## IC07_IC16                  1.2146139               0.226302929
## IC08_IC11                 -0.7203461               0.472362955
## IC08_IC20                  0.3857991               0.700157941
## IC11_IC12                  1.4575925               0.146912831
## IC12_IC17                  2.1780533               0.030866033
## IC13_IC14                 -2.1748957               0.031106239
## IC14_IC20                  1.1054536               0.270622575
## IC15_IC17                 -1.2588909               0.209903372
## IC17_IC18                  2.4069749               0.017224053
##           SCoverRRB_Disc_vs_TD.es SCoverRRB_Disc_vs_TD.AIC
## IC01_IC12              0.23334387                -8.211494
## IC03_IC13             -0.49830010               -96.166936
## IC04_IC11              0.03303592               175.916360
## IC04_IC12             -0.31221826                25.172009
## IC05_IC06             -0.05644977               120.570260
## IC05_IC19             -0.14220895               113.755358
## IC07_IC13              0.23063765               -37.883659
## IC07_IC16             -0.18861642               113.526107
## IC08_IC11              0.15722966               103.191544
## IC08_IC20             -0.06295329               -56.327164
## IC11_IC12             -0.25226542                21.609438
## IC12_IC17             -0.38329438                62.741737
## IC13_IC14              0.38549480              -179.523638
## IC14_IC20             -0.23771320              -187.317672
## IC15_IC17              0.24070307                30.258310
## IC17_IC18             -0.40645726                59.316748
##           SCoverRRB_Disc_vs_TD.BIC SCoverRRB_Rep_vs_TD.tstat
## IC01_IC12                 10.38770                -0.1911435
## IC03_IC13                -77.56774                 2.2155916
## IC04_IC11                194.51556                 0.7650052
## IC04_IC12                 43.77121                 0.4145599
## IC05_IC06                139.16946                -2.1094196
## IC05_IC19                132.35456                 1.4531123
## IC07_IC13                -19.28446                -2.3883109
## IC07_IC16                132.12531                 0.3470195
## IC08_IC11                121.79074                -0.2706070
## IC08_IC20                -37.72797                -0.2163795
## IC11_IC12                 40.20864                 2.5671076
## IC12_IC17                 81.34094                 2.3858770
## IC13_IC14               -160.92444                -0.4694337
## IC14_IC20               -168.71847                 1.4884527
## IC15_IC17                 48.85751                 0.5090827
## IC17_IC18                 77.91595                 1.4006002
##           SCoverRRB_Rep_vs_TD.pval SCoverRRB_Rep_vs_TD.es
## IC01_IC12               0.84865397             0.04046754
## IC03_IC13               0.02812249            -0.36583152
## IC04_IC11               0.44538832            -0.15597163
## IC04_IC12               0.67901597            -0.07585451
## IC05_IC06               0.03645500             0.36291603
## IC05_IC19               0.14813889            -0.25619906
## IC07_IC13               0.01808414             0.42431630
## IC07_IC16               0.72902985            -0.03451740
## IC08_IC11               0.78703994             0.05217370
## IC08_IC20               0.82896589             0.09501337
## IC11_IC12               0.01116553            -0.41918679
## IC12_IC17               0.01820003            -0.37987412
## IC13_IC14               0.63939478             0.12909039
## IC14_IC20               0.13858766            -0.23672652
## IC15_IC17               0.61139122            -0.03857520
## IC17_IC18               0.16325800            -0.21556482
##           SCoverRRB_Rep_vs_TD.AIC SCoverRRB_Rep_vs_TD.BIC SCoverRRB.repBF
## IC01_IC12               -21.64452               -3.008849       0.4690143
## IC03_IC13              -120.59743             -101.961762       6.9485035
## IC04_IC11               205.05179              223.687466       0.7257347
## IC04_IC12                44.64199               63.277667       0.5209997
## IC05_IC06               116.35188              134.987555       1.4328289
## IC05_IC19               116.36092              134.996593       1.8687764
## IC07_IC13               -74.01734              -55.381662       9.8489366
## IC07_IC16               121.08427              139.719939       0.6219226
## IC08_IC11                68.85415               87.489827       0.6957798
## IC08_IC20               -98.79765              -80.161977       0.6610975
## IC11_IC12               -50.51656              -31.880887      13.3782336
## IC12_IC17                10.11203               28.747702      11.5978596
## IC13_IC14              -179.33802             -160.702346       0.3826019
## IC14_IC20              -266.49051             -247.854835       2.0574500
## IC15_IC17                66.72561               85.361279       0.3687863
## IC17_IC18                40.29085               58.926524       1.4573308
##           SCequalRRB_Disc_vs_SCoverRRB.tstat SCequalRRB_Disc_vs_SCoverRRB.pval
## IC01_IC12                         1.06562973                         0.2887305
## IC03_IC13                        -0.92747309                         0.3555427
## IC04_IC11                         0.07578615                         0.9397154
## IC04_IC12                        -0.41171568                         0.6812823
## IC05_IC06                        -1.27865794                         0.2034849
## IC05_IC19                        -0.06885305                         0.9452212
## IC07_IC13                        -0.56891543                         0.5704768
## IC07_IC16                        -0.13229628                         0.8949715
## IC08_IC11                         0.84130353                         0.4018513
## IC08_IC20                        -0.38838580                         0.6984187
## IC11_IC12                        -0.56107794                         0.5757906
## IC12_IC17                        -1.34367581                         0.1815885
## IC13_IC14                         0.70244557                         0.4837617
## IC14_IC20                        -0.19706352                         0.8441112
## IC15_IC17                         0.36487654                         0.7158458
## IC17_IC18                         0.66242596                         0.5089685
##           SCequalRRB_Disc_vs_SCoverRRB.es SCequalRRB_Disc_vs_SCoverRRB.AIC
## IC01_IC12                     -0.20786490                        -2.749578
## IC03_IC13                      0.14065762                       -41.817613
## IC04_IC11                     -0.02222239                       122.504440
## IC04_IC12                      0.05024782                        65.322676
## IC05_IC06                      0.24020480                       108.451300
## IC05_IC19                      0.00107773                       144.261739
## IC07_IC13                      0.12504641                       -19.184422
## IC07_IC16                      0.02789699                        98.481402
## IC08_IC11                     -0.12354558                        86.852640
## IC08_IC20                      0.07334816                        -6.378542
## IC11_IC12                      0.07451368                         2.251464
## IC12_IC17                      0.23336423                        53.568095
## IC13_IC14                     -0.10782769                      -166.874716
## IC14_IC20                      0.02781499                       -89.164877
## IC15_IC17                     -0.04716789                        34.078033
## IC17_IC18                     -0.11368454                        11.006911
##           SCequalRRB_Disc_vs_SCoverRRB.BIC SCequalRRB_Rep_vs_SCoverRRB.tstat
## IC01_IC12                        14.123528                      -0.036769258
## IC03_IC13                       -24.944507                      -0.089286819
## IC04_IC11                       139.377546                      -0.694297702
## IC04_IC12                        82.195782                       1.588992840
## IC05_IC06                       125.324406                      -0.467778230
## IC05_IC19                       161.134845                      -0.771857298
## IC07_IC13                        -2.311315                      -0.500517418
## IC07_IC16                       115.354508                      -0.969364134
## IC08_IC11                       103.725746                       0.881997843
## IC08_IC20                        10.494564                       1.966233011
## IC11_IC12                        19.124570                      -1.546259600
## IC12_IC17                        70.441202                      -0.418141665
## IC13_IC14                      -150.001610                      -1.820714277
## IC14_IC20                       -72.291771                       0.713906068
## IC15_IC17                        50.951139                       0.008702881
## IC17_IC18                        27.880017                       0.853037598
##           SCequalRRB_Rep_vs_SCoverRRB.pval SCequalRRB_Rep_vs_SCoverRRB.es
## IC01_IC12                       0.97072764                    0.028127395
## IC03_IC13                       0.92899687                    0.007794058
## IC04_IC11                       0.48878391                    0.089084628
## IC04_IC12                       0.11458819                   -0.276865834
## IC05_IC06                       0.64075772                    0.101191130
## IC05_IC19                       0.44165573                    0.141760026
## IC07_IC13                       0.61759091                    0.084157259
## IC07_IC16                       0.33423545                    0.165803830
## IC08_IC11                       0.37947088                   -0.136005123
## IC08_IC20                       0.05148792                   -0.311009261
## IC11_IC12                       0.12456993                    0.291036546
## IC12_IC17                       0.67656101                    0.058276698
## IC13_IC14                       0.07104226                    0.408021997
## IC14_IC20                       0.47661617                   -0.162348541
## IC15_IC17                       0.99307007                    0.011773670
## IC17_IC18                       0.39527006                   -0.142619611
##           SCequalRRB_Rep_vs_SCoverRRB.AIC SCequalRRB_Rep_vs_SCoverRRB.BIC
## IC01_IC12                       -2.920641                    1.419154e+01
## IC03_IC13                      -75.964605                   -5.885242e+01
## IC04_IC11                      123.878930                    1.409911e+02
## IC04_IC12                       46.097716                    6.320990e+01
## IC05_IC06                      108.442552                    1.255547e+02
## IC05_IC19                      140.018742                    1.571309e+02
## IC07_IC13                      -24.551099                   -7.438918e+00
## IC07_IC16                       72.680178                    8.979236e+01
## IC08_IC11                       80.713383                    9.782556e+01
## IC08_IC20                      -17.109353                    2.828336e-03
## IC11_IC12                        3.424649                    2.053683e+01
## IC12_IC17                       45.612007                    6.272419e+01
## IC13_IC14                     -153.238002                   -1.361258e+02
## IC14_IC20                     -115.254969                   -9.814279e+01
## IC15_IC17                       51.604858                    6.871704e+01
## IC17_IC18                       44.245333                    6.135751e+01
##           SCequalRRB_vs_SCoverRRB.repBF SCcorr_Disc.r SCcorr_Disc.t
## IC01_IC12                     0.5179629    0.02194196    0.42665054
## IC03_IC13                     0.5894995   -0.21565303   -2.15128320
## IC04_IC11                     0.7740118    0.05368490    0.96235535
## IC04_IC12                     0.9164447   -0.04308262   -0.32111128
## IC05_IC06                     0.6625575   -0.02776040   -0.54631033
## IC05_IC19                     0.8396703    0.08944708    1.03701535
## IC07_IC13                     0.7959574    0.02825143    0.25964948
## IC07_IC16                     0.9472991    0.04398889    0.30052154
## IC08_IC11                     1.0372300    0.11614033    1.60916326
## IC08_IC20                     1.2178738    0.06102096    0.48328981
## IC11_IC12                     1.8221373   -0.09722395   -0.95708854
## IC12_IC17                     0.6164800   -0.09815559   -1.12468193
## IC13_IC14                     0.7524868   -0.05987545   -0.39990554
## IC14_IC20                     0.7378927    0.10943481    0.72915968
## IC15_IC17                     0.6819499    0.03926970    0.07988827
## IC17_IC18                     1.0046741    0.12777685    1.07803010
##           SCcorr_Disc.pval SCcorr_Rep.r SCcorr_Rep.t SCcorr_Rep.pval
## IC01_IC12        0.6703983  0.003233085   0.15461485      0.87737433
## IC03_IC13        0.0334581 -0.126963611  -1.12300938      0.26358581
## IC04_IC11        0.3378064 -0.018351087  -0.18924902      0.85020459
## IC04_IC12        0.7486846  0.008786831   0.44549366      0.65673363
## IC05_IC06        0.5858669  0.009072133  -0.13993955      0.88893298
## IC05_IC19        0.3018139 -0.036191267  -0.34401951      0.73140964
## IC07_IC13        0.7955791  0.056376570   0.26710893      0.78982547
## IC07_IC16        0.7642994 -0.018554199  -0.19095518      0.84887051
## IC08_IC11        0.1102083  0.187959425   2.27255249      0.02476101
## IC08_IC20        0.6297706  0.200556463   2.05566773      0.04189683
## IC11_IC12        0.3404468 -0.124975082  -1.24159464      0.21671088
## IC12_IC17        0.2629685 -0.203831211  -2.31675788      0.02214415
## IC13_IC14        0.6899370 -0.016340357   0.07893432      0.93721109
## IC14_IC20        0.4673248 -0.034469408  -0.67167717      0.50302870
## IC15_IC17        0.9364592 -0.068463813  -1.08495884      0.28002795
## IC17_IC18        0.2831826 -0.097275106  -1.20345834      0.23107323
##           SCcorr.repBF RRBcorr_Disc.r RRBcorr_Disc.t RRBcorr_Disc.pval
## IC01_IC12    0.6948175    0.022253885     0.41920559        0.67581534
## IC03_IC13    0.9932646   -0.100984042    -0.98383037        0.32717839
## IC04_IC11    0.5092501    0.001223994     0.85033233        0.39683381
## IC04_IC12    0.6679560   -0.018878034    -0.10172859        0.91914190
## IC05_IC06    0.6784365    0.129883126     1.02441277        0.30770101
## IC05_IC19    0.4593061    0.111258537     1.19479533        0.23452312
## IC07_IC13    0.7257912    0.006504303     0.17706303        0.85975739
## IC07_IC16    0.6716151    0.046963448     0.01189321        0.99053057
## IC08_IC11    8.0951621    0.140225485     1.70980028        0.08988736
## IC08_IC20    3.1180687    0.004305451    -0.25344479        0.80035822
## IC11_IC12    1.4839549   -0.065262479    -0.60958908        0.54328656
## IC12_IC17    7.0608340    0.268013929     2.12742673        0.03543136
## IC13_IC14    0.6627342   -0.033549747    -0.07222455        0.94254345
## IC14_IC20    0.5366952    0.059448943     0.13091575        0.89606118
## IC15_IC17    0.9027455    0.059630717     0.24894077        0.80383217
## IC17_IC18    0.3932325    0.066930613     0.21592829        0.82941025
##           RRBcorr_Rep.r RRBcorr_Rep.t RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12  -0.031570729    0.43793056      0.662192433     0.7714689
## IC03_IC13  -0.105545423   -0.97743952      0.330239493     1.1275916
## IC04_IC11  -0.006080479    0.69246450      0.489930085     0.8842338
## IC04_IC12  -0.132022831   -1.28323728      0.201782973     1.1336710
## IC05_IC06   0.008273737    0.01175331      0.990641170     0.5386826
## IC05_IC19   0.020045801    0.12026905      0.904463122     0.5254590
## IC07_IC13   0.080865498    0.37982124      0.704722665     0.7454881
## IC07_IC16  -0.065311241   -1.07680757      0.283640056     0.9349332
## IC08_IC11   0.234578549    2.30655588      0.022725465     8.8949014
## IC08_IC20   0.135383657    1.14996703      0.252352808     0.8340661
## IC11_IC12  -0.029488716   -0.56718002      0.571609043     0.8210033
## IC12_IC17  -0.111746204   -0.72156532      0.471909306     0.1191160
## IC13_IC14   0.240763309    2.83555055      0.005337477     4.5660604
## IC14_IC20  -0.196980403   -2.08910559      0.038726525     1.8112695
## IC15_IC17   0.181375760    1.51968554      0.131115427     1.4936044
## IC17_IC18  -0.131431759   -1.21259283      0.227572306     0.8830450
##           SumSCRRB_Disc.r SumSCRRB_Disc.t SumSCRRB_Disc.pval SumSCRRB_Rep.r
## IC01_IC12      0.02901956       0.5310098         0.59639342    -0.01216495
## IC03_IC13     -0.21386179      -1.9781943         0.05019802    -0.14306998
## IC04_IC11      0.03906505       1.0959706         0.27528630    -0.01646374
## IC04_IC12     -0.04208466      -0.2700538         0.78758276    -0.05450550
## IC05_IC06      0.05825782       0.2156622         0.82961723     0.01057013
## IC05_IC19      0.13048683       1.3482337         0.18012260    -0.01768556
## IC07_IC13      0.02407416       0.2742754         0.78434455     0.07926642
## IC07_IC16      0.05957621       0.2202769         0.82602984    -0.04395750
## IC08_IC11      0.16676443       2.0117014         0.04649155     0.24768499
## IC08_IC20      0.04615055       0.1748611         0.86148346     0.21130671
## IC11_IC12     -0.10848355      -0.9888179         0.32474187    -0.10658917
## IC12_IC17      0.09204190       0.4721155         0.63770172    -0.20289733
## IC13_IC14     -0.06286582      -0.3133240         0.75457845     0.09966974
## IC14_IC20      0.11370131       0.5391124         0.59080811    -0.11684976
## IC15_IC17      0.06380923       0.2176714         0.82805483     0.03307955
## IC17_IC18      0.13122882       0.8776867         0.38186762    -0.13299202
##           SumSCRRB_Rep.t SumSCRRB_Rep.pval SumSCRRB.repBF   zds_Disc.r
## IC01_IC12     0.32693046       0.744267295      0.7302895  0.002657793
## IC03_IC13    -1.27302692       0.205371003      1.3723820 -0.107756293
## IC04_IC11     0.09844675       0.921735250      0.5458223  0.043233647
## IC04_IC12    -0.35513921       0.723083828      0.7446164 -0.022345781
## IC05_IC06    -0.09186411       0.926953069      0.6864205 -0.112569212
## IC05_IC19    -0.18477365       0.853706029      0.3920961 -0.003416208
## IC07_IC13     0.36927339       0.712548753      0.7487993  0.018705713
## IC07_IC16    -0.61417528       0.540215720      0.7123406  0.003703727
## IC08_IC11     2.70863823       0.007703423     22.7349100 -0.001563469
## IC08_IC20     2.02789026       0.044697063      2.3254318  0.047133160
## IC11_IC12    -1.12832858       0.261342137      1.3174932 -0.034806562
## IC12_IC17    -1.93457578       0.055301730      1.0794209 -0.266888039
## IC13_IC14     1.46765418       0.144709887      0.9382909 -0.026012431
## IC14_IC20    -1.47966288       0.141479102      0.7619345  0.048866197
## IC15_IC17    -0.03661856       0.970847557      0.6886295 -0.008920903
## IC17_IC18    -1.41513365       0.159515783      0.5152166  0.058791490
##            zds_Disc.t zds_Disc.pval    zds_Rep.r    zds_Rep.t zds_Rep.pval
## IC01_IC12  0.02685358   0.978621128  0.023309478 -0.158594291   0.87424447
## IC03_IC13 -0.99853453   0.320029498 -0.062880426 -0.430408517   0.66763968
## IC04_IC11  0.15616824   0.876162830 -0.014899531 -0.587190628   0.55813481
## IC04_IC12 -0.19469984   0.845957187  0.092781160  1.438981648   0.15265506
## IC05_IC06 -1.30640519   0.193913203  0.004027948 -0.149721114   0.88122595
## IC05_IC19 -0.11501962   0.908621815 -0.049681652 -0.445095420   0.65702061
## IC07_IC13  0.08289200   0.934075525  0.006338976 -0.005453223   0.99565767
## IC07_IC16  0.27075993   0.787040843  0.022444169  0.503936006   0.61519338
## IC08_IC11 -0.04063034   0.967658120  0.042788914  0.643662229   0.52097367
## IC08_IC20  0.60851801   0.543994106  0.119371439  1.294230664   0.19797173
## IC11_IC12 -0.33824990   0.735765943 -0.109081175 -0.878497723   0.38135921
## IC12_IC17 -2.80788842   0.005822396 -0.137767387 -1.880084783   0.06242402
## IC13_IC14 -0.28190050   0.778505391 -0.170165849 -1.763193184   0.08031152
## IC14_IC20  0.54296251   0.588162711  0.090168120  0.851331964   0.39621295
## IC15_IC17 -0.11415196   0.909308101 -0.185111864 -2.076675164   0.03988013
## IC17_IC18  0.73741185   0.462311380 -0.015996310 -0.353999369   0.72393577
##           zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Disc.t
## IC01_IC12 0.7030830                0.039682918                 0.34837115
## IC03_IC13 0.7055483               -0.207728240                -1.51177932
## IC04_IC11 0.7267282                0.049528691                 0.93278094
## IC04_IC12 1.0202064                0.057855158                 0.28564671
## IC05_IC06 0.5023456                0.080266276                 0.09158388
## IC05_IC19 0.7538494                0.237036965                 1.47165666
## IC07_IC13 0.6990597               -0.078614861                -0.36252382
## IC07_IC16 0.7858125               -0.007431483                -0.40569269
## IC08_IC11 0.7680143                0.163775449                 1.80008504
## IC08_IC20 1.4445651                0.109778625                 0.49430368
## IC11_IC12 0.9614569               -0.169326009                -1.17277260
## IC12_IC17 3.1856173                0.133243166                 0.97994473
## IC13_IC14 1.9247746               -0.246526697                -1.49077308
## IC14_IC20 0.9854177                0.086094875                 0.34126300
## IC15_IC17 2.3089681                0.010249859                -0.04220945
## IC17_IC18 0.5526155                0.136020835                 0.19633520
##           SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12                    0.72856415              -0.053275570
## IC03_IC13                    0.13490716              -0.066990666
## IC04_IC11                    0.35400805              -0.060997222
## IC04_IC12                    0.77595768              -0.214238350
## IC05_IC06                    0.92727951              -0.044488707
## IC05_IC19                    0.14541166              -0.004206571
## IC07_IC13                    0.71800810               0.128479115
## IC07_IC16                    0.68615507              -0.043633285
## IC08_IC11                    0.07597868               0.235325204
## IC08_IC20                    0.62257597               0.225446130
## IC11_IC12                    0.24469978              -0.219868936
## IC12_IC17                    0.33034995              -0.228755573
## IC13_IC14                    0.14032965               0.321690324
## IC14_IC20                    0.73388593              -0.221042415
## IC15_IC17                    0.96644696               0.144977010
## IC17_IC18                    0.84489336              -0.136682732
##           SumSCRRB_SCequalRRB_Rep.t SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12               -0.03895442                  0.969026260
## IC03_IC13               -0.53514792                  0.594070027
## IC04_IC11               -0.11922934                  0.905400209
## IC04_IC12               -1.61758166                  0.109790015
## IC05_IC06               -0.36941948                  0.712816095
## IC05_IC19               -0.07250088                  0.942388897
## IC07_IC13                0.95691508                  0.341566696
## IC07_IC16               -0.54667412                  0.586163160
## IC08_IC11                1.71806861                  0.089751588
## IC08_IC20                1.62206712                  0.108824438
## IC11_IC12               -1.68178233                  0.096610878
## IC12_IC17               -1.44404327                  0.152731648
## IC13_IC14                2.76041435                  0.007192758
## IC14_IC20               -1.68507731                  0.095970869
## IC15_IC17                0.94662057                  0.346755585
## IC17_IC18               -0.75618949                  0.451813426
##           SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Disc.t
## IC01_IC12                 0.6745920          -0.277097910           -2.35339866
## IC03_IC13                 0.6301976           0.045777149            0.52417209
## IC04_IC11                 0.5331019          -0.067843384           -0.98272177
## IC04_IC12                 1.0552972           0.005012945            0.10919400
## IC05_IC06                 0.7119774          -0.075535981           -0.29879596
## IC05_IC19                 0.3825588           0.081871832            0.61226453
## IC07_IC13                 0.7189493          -0.122625417           -1.18731960
## IC07_IC16                 0.8100174           0.132853464            1.45896946
## IC08_IC11                 3.0247951          -0.015949137           -0.49102809
## IC08_IC20                 1.9093969           0.172856818            2.07638632
## IC11_IC12                 2.6949289           0.092674386            0.63635602
## IC12_IC17                 0.4622104          -0.174141875           -1.26314580
## IC13_IC14                 0.3513133          -0.281095151           -2.31704245
## IC14_IC20                 1.0462919           0.066592468            0.56560023
## IC15_IC17                 0.8632137           0.012884871            0.02229822
## IC17_IC18                 0.7449407           0.020846790            0.74653486
##           zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.r zds_SCequalRRB_Rep.t
## IC01_IC12               0.02129821           0.01222164           -0.1439936
## IC03_IC13               0.60174683          -0.06574081           -0.4407915
## IC04_IC11               0.32899026           0.01496057           -0.1160502
## IC04_IC12               0.91334822           0.02898635            0.4189487
## IC05_IC06               0.76594467           0.03980576            0.2586047
## IC05_IC19               0.54226522           0.03859306            0.4147816
## IC07_IC13               0.23895077           0.09251081            0.6778012
## IC07_IC16               0.14886315           0.26400831            2.5220961
## IC08_IC11               0.62487955           0.06642119            0.8069976
## IC08_IC20               0.04137936          -0.07261177           -0.5923550
## IC11_IC12               0.52653471           0.07345652            0.8767629
## IC12_IC17               0.21055635          -0.25547619           -2.4806166
## IC13_IC14               0.02330946           0.04072736            0.2928297
## IC14_IC20               0.57339998           0.09615702            0.7012499
## IC15_IC17               0.98227091          -0.28381070           -2.5616960
## IC17_IC18               0.45774195          -0.16083034           -1.6652398
##           zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF zds_SCoverRRB_Disc.r
## IC01_IC12              0.88587691            0.2054507           0.14054072
## IC03_IC13              0.66058325            0.6102065          -0.34326125
## IC04_IC11              0.90791104            0.5822046           0.18211389
## IC04_IC12              0.67640506            0.7476418          -0.09961334
## IC05_IC06              0.79662149            0.6694213          -0.15658841
## IC05_IC19              0.67944038            0.7548410          -0.20957605
## IC07_IC13              0.49990344            0.3685000           0.53192645
## IC07_IC16              0.01370250           12.2238649           0.04521700
## IC08_IC11              0.42212180            0.6386348          -0.27888369
## IC08_IC20              0.55532623            0.1392755           0.05261409
## IC11_IC12              0.38330777            1.0154272          -0.11466004
## IC12_IC17              0.01526879           10.1767698          -0.53560195
## IC13_IC14              0.77042993            0.1313312           0.05823789
## IC14_IC20              0.48523399            0.8925094           0.14695973
## IC15_IC17              0.01234372            3.4568789          -0.23401579
## IC17_IC18              0.09987684            0.6589320           0.10168553
##           zds_SCoverRRB_Disc.t zds_SCoverRRB_Disc.pval zds_SCoverRRB_Rep.r
## IC01_IC12          0.794771650            0.4314369522       -0.0060153582
## IC03_IC13         -2.183966561            0.0348879192       -0.0749572171
## IC04_IC11          1.430845263            0.1602410613        0.0633555175
## IC04_IC12         -0.341021583            0.7348721727       -0.0044682949
## IC05_IC06         -0.759347862            0.4520965008        0.0519226751
## IC05_IC19         -1.060219299            0.2954049687       -0.0004208548
## IC07_IC13          3.575314279            0.0009321468        0.0557533273
## IC07_IC16          0.002796665            0.9977824883        0.0558002703
## IC08_IC11         -1.633811953            0.1101459505       -0.1777350289
## IC08_IC20          0.178470459            0.8592543754       -0.0184373130
## IC11_IC12         -0.455418316            0.6512709114       -0.0075125849
## IC12_IC17         -3.624413065            0.0008085294       -0.1655617227
## IC13_IC14          0.132306124            0.8954054759       -0.1726519419
## IC14_IC20          0.959712817            0.3429652836       -0.1854524594
## IC15_IC17         -1.764187190            0.0853366697       -0.4151432130
## IC17_IC18          0.600416678            0.5516154764       -0.1505652622
##           zds_SCoverRRB_Rep.t zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF
## IC01_IC12         -0.18371738             0.85516334          0.55898342
## IC03_IC13         -0.58103842             0.56447437          0.42907591
## IC04_IC11          0.30987127             0.75826752          0.53190112
## IC04_IC12         -0.01676234             0.98670955          0.68111452
## IC05_IC06          0.47240609             0.63920430          0.53475443
## IC05_IC19         -0.08265380             0.93453906          0.55014177
## IC07_IC13          0.46076194             0.64746484          0.06811246
## IC07_IC16          0.49752505             0.62154294          0.74628094
## IC08_IC11         -1.25012638             0.21851790          1.46137709
## IC08_IC20         -0.07952432             0.93701212          0.69110033
## IC11_IC12          0.04006744             0.96823868          0.65812655
## IC12_IC17         -0.97548258             0.33518372          0.19293903
## IC13_IC14         -1.14337452             0.25968014          0.90266897
## IC14_IC20         -1.02958799             0.30938917          0.44372222
## IC15_IC17         -2.59716095             0.01308996         16.31276435
## IC17_IC18         -0.84909502             0.40088167          0.59413399
##           VinelandABC_Disc.r VinelandABC_Disc.t VinelandABC_Disc.pval
## IC01_IC12        0.117928538         1.28839305            0.20008779
## IC03_IC13       -0.134772608        -1.21171075            0.22800372
## IC04_IC11       -0.103254277        -1.19071242            0.23611656
## IC04_IC12        0.002441248         0.09422101            0.92509074
## IC05_IC06        0.057668657         0.34139602            0.73340254
## IC05_IC19       -0.143747879        -1.51313280            0.13287524
## IC07_IC13        0.073694498         0.64281122            0.52157295
## IC07_IC16        0.029103565         0.47801451            0.63350953
## IC08_IC11       -0.041406876        -0.28936272            0.77280316
## IC08_IC20       -0.012128605        -0.05912888            0.95294781
## IC11_IC12        0.056106901         0.59532805            0.55274527
## IC12_IC17        0.016795507         0.21690377            0.82865168
## IC13_IC14        0.025951362         0.40944604            0.68294229
## IC14_IC20       -0.182685897        -2.06849525            0.04074162
## IC15_IC17       -0.025714298        -0.44972704            0.65371858
## IC17_IC18       -0.162183284        -1.71739838            0.08848605
##           VinelandABC_Rep.r VinelandABC_Rep.t VinelandABC_Rep.pval
## IC01_IC12        0.17706471         1.8423656           0.06779140
## IC03_IC13        0.07548509         0.9979447           0.32023409
## IC04_IC11       -0.11207528        -1.2553148           0.21170617
## IC04_IC12       -0.13179877        -1.2248855           0.22292159
## IC05_IC06        0.03961356         0.3103404           0.75681897
## IC05_IC19       -0.21193640        -2.2455797           0.02648763
## IC07_IC13       -0.04636066        -0.6413866           0.52244583
## IC07_IC16       -0.01998481        -0.1752815           0.86114208
## IC08_IC11        0.00709269         0.3005463           0.76425978
## IC08_IC20       -0.21462466        -2.4143802           0.01721091
## IC11_IC12        0.05621054         0.7092251           0.47950555
## IC12_IC17        0.05329079         0.3791170           0.70524422
## IC13_IC14        0.02675679         0.3433432           0.73191706
## IC14_IC20       -0.21801342        -2.5502905           0.01197059
## IC15_IC17       -0.09745959        -1.2025739           0.23141424
## IC17_IC18       -0.06527082        -0.8748833           0.38331532
##           VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12         3.5202257                    0.14464438
## IC03_IC13         0.3407267                   -0.19066515
## IC04_IC11         1.5352527                   -0.10451093
## IC04_IC12         0.9658800                   -0.07515700
## IC05_IC06         0.7347593                    0.12988458
## IC05_IC19         7.4442016                   -0.21402719
## IC07_IC13         0.5696654                    0.11911550
## IC07_IC16         0.6392246                   -0.12561640
## IC08_IC11         0.6716700                   -0.29797757
## IC08_IC20         3.2101596                   -0.10977147
## IC11_IC12         0.8988975                   -0.14229878
## IC12_IC17         0.7474660                   -0.11495606
## IC13_IC14         0.7420257                    0.01571556
## IC14_IC20        16.1724744                   -0.26680425
## IC15_IC17         1.2591897                    0.07376916
## IC17_IC18         0.8510854                   -0.09863034
##           VinelandABC_SCequalRRB_Disc.t VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12                     0.9824985                       0.32909942
## IC03_IC13                    -1.2045644                       0.23226235
## IC04_IC11                    -0.8232033                       0.41307359
## IC04_IC12                    -0.4946500                       0.62233264
## IC05_IC06                     0.7821750                       0.43664151
## IC05_IC19                    -1.6285163                       0.10772391
## IC07_IC13                     0.6710248                       0.50432242
## IC07_IC16                    -0.5420238                       0.58945283
## IC08_IC11                    -2.1541049                       0.03453314
## IC08_IC20                    -0.5812687                       0.56284984
## IC11_IC12                    -1.0911793                       0.27878247
## IC12_IC17                    -0.6009245                       0.54975165
## IC13_IC14                     0.3515030                       0.72622361
## IC14_IC20                    -2.0528896                       0.04366782
## IC15_IC17                     0.2685229                       0.78905427
## IC17_IC18                    -0.7449358                       0.45870215
##           VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Rep.t
## IC01_IC12                 0.0009291509                  -0.10969434
## IC03_IC13                -0.0221926870                  -0.01260826
## IC04_IC11                 0.0961412273                   0.66337716
## IC04_IC12                -0.1485553903                  -1.00402502
## IC05_IC06                 0.1875840886                   1.45085120
## IC05_IC19                -0.3046567090                  -2.60968217
## IC07_IC13                -0.0787165021                  -0.81352967
## IC07_IC16                 0.0383986453                   0.43953326
## IC08_IC11                 0.1036129040                   1.12949430
## IC08_IC20                -0.3571061284                  -3.22372162
## IC11_IC12                 0.0684858666                   0.89259738
## IC12_IC17                 0.0574014139                   0.39808593
## IC13_IC14                 0.0914518972                   0.91743478
## IC14_IC20                -0.2651582112                  -2.41475934
## IC15_IC17                -0.0624815497                  -0.77287710
## IC17_IC18                -0.0708768645                  -0.77256392
##           VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12                     0.912933526                   0.52046990
## IC03_IC13                     0.989972521                   0.48753775
## IC04_IC11                     0.509044927                   0.50214093
## IC04_IC12                     0.318471172                   1.09199286
## IC05_IC06                     0.150831159                   1.80317495
## IC05_IC19                     0.010860882                  15.81255120
## IC07_IC13                     0.418390930                   0.56243403
## IC07_IC16                     0.661490557                   0.60558343
## IC08_IC11                     0.262151007                   0.09098352
## IC08_IC20                     0.001848388                  20.11427182
## IC11_IC12                     0.374818376                   0.38903697
## IC12_IC17                     0.691654359                   0.59046050
## IC13_IC14                     0.361742943                   0.99009805
## IC14_IC20                     0.018087248                  11.96639023
## IC15_IC17                     0.441930920                   0.72343255
## IC17_IC18                     0.442115217                   0.94455160
##           VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Disc.t
## IC01_IC12                   0.15885195                    1.0367605
## IC03_IC13                  -0.08587292                   -0.4067815
## IC04_IC11                  -0.11135463                   -0.6644157
## IC04_IC12                   0.08946024                    0.6635882
## IC05_IC06                  -0.02039252                   -0.1207941
## IC05_IC19                  -0.10210360                   -0.4131089
## IC07_IC13                   0.08133614                    0.2642324
## IC07_IC16                   0.18265234                    1.1511466
## IC08_IC11                   0.20768412                    1.5710969
## IC08_IC20                   0.09966423                    0.4156872
## IC11_IC12                   0.27610173                    2.0077711
## IC12_IC17                   0.17458268                    1.0564789
## IC13_IC14                   0.01658595                    0.2251281
## IC14_IC20                  -0.03275164                   -0.3627840
## IC15_IC17                  -0.08710577                   -0.6114066
## IC17_IC18                  -0.28114540                   -1.9294988
##           VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.r
## IC01_IC12                      0.30607461                 0.540836261
## IC03_IC13                      0.68633693                 0.172613120
## IC04_IC11                      0.51023658                -0.454390194
## IC04_IC12                      0.51076065                -0.126702425
## IC05_IC06                      0.90445884                -0.156913702
## IC05_IC19                      0.68173309                -0.069523596
## IC07_IC13                      0.79295754                -0.012187289
## IC07_IC16                      0.25650672                -0.159445476
## IC08_IC11                      0.12403778                -0.189792284
## IC08_IC20                      0.67986057                 0.124610150
## IC11_IC12                      0.05145535                -0.033979460
## IC12_IC17                      0.29708868                -0.004087625
## IC13_IC14                      0.82302587                -0.140739306
## IC14_IC20                      0.71867627                -0.054085220
## IC15_IC17                      0.54438979                -0.159547623
## IC17_IC18                      0.06078353                -0.054271484
##           VinelandABC_SCoverRRB_Rep.t VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12                 3.901955299                   0.0003568259
## IC03_IC13                 1.278292374                   0.2085142873
## IC04_IC11                -3.099449158                   0.0035422004
## IC04_IC12                -0.755126483                   0.4545966129
## IC05_IC06                -1.074770171                   0.2889182338
## IC05_IC19                -0.230339680                   0.8190018227
## IC07_IC13                -0.156498546                   0.8764272855
## IC07_IC16                -1.212629240                   0.2323850711
## IC08_IC11                -1.041616360                   0.3038445676
## IC08_IC20                 0.673357137                   0.5045923698
## IC11_IC12                -0.166130682                   0.8688910085
## IC12_IC17                -0.001428922                   0.9988669887
## IC13_IC14                -0.820378260                   0.4168627071
## IC14_IC20                -0.406499341                   0.6865425470
## IC15_IC17                -1.079280129                   0.2869280780
## IC17_IC18                -0.502289832                   0.6182177344
##           VinelandABC_SCoverRRB.repBF
## IC01_IC12                 140.8582916
## IC03_IC13                   0.7860256
## IC04_IC11                  17.9491075
## IC04_IC12                   0.5646209
## IC05_IC06                   0.9982184
## IC05_IC19                   0.7129276
## IC07_IC13                   0.6780060
## IC07_IC16                   0.3612854
## IC08_IC11                   0.2193664
## IC08_IC20                   0.8661881
## IC11_IC12                   0.2148602
## IC12_IC17                   0.5261220
## IC13_IC14                   0.7469726
## IC14_IC20                   0.7600222
## IC15_IC17                   1.1920280
## IC17_IC18                   0.4702474
mask1 = aovres$SCequalRRB.repBF>=10
mask2 = aovres$SCoverRRB.repBF>=10
# mask3 = aovres$RRBoverSC.repBF>=10
mask4 = aovres$SCequalRRB_vs_SCoverRRB.repBF>=10
mask5 = aovres$SCcorr.repBF>=10
mask6 = aovres$RRBcorr.repBF>=10
mask7 = aovres$SumSCRRB.repBF>=10
mask8 = aovres$zds.repBF>=10
mask9 = aovres$zds_SCequalRRB.repBF>=10
mask10 = aovres$zds_SCoverRRB.repBF>=10
mask11 = aovres$SumSCRRB_SCequalRRB.repBF>=10
mask12 = aovres$VinelandABC.repBF>=10
mask13 = aovres$VinelandABC_SCequalRRB.repBF>=10
mask14 = aovres$VinelandABC_SCoverRRB.repBF>=10
mask_allBF = mask1 | mask2 | mask4 | mask5 | mask6 | mask7 | mask8 | mask9 | mask10 | mask11 | mask12 | mask13 | mask14

aovres[mask_allBF,c("compNames","SCequalRRB.repBF","SCoverRRB.repBF",
                    "SCcorr_Disc.r","SCcorr_Rep.r","SCcorr_Disc.pval","SCcorr_Rep.pval","SCcorr.repBF",
                    "RRBcorr_Disc.r","RRBcorr_Rep.r","RRBcorr_Disc.pval","RRBcorr_Rep.pval","RRBcorr.repBF",
                    "SumSCRRB_Disc.r","SumSCRRB_Rep.r","SumSCRRB_Disc.pval","SumSCRRB_Rep.pval","SumSCRRB.repBF",
                    "zds_Disc.r","zds_Rep.r","zds_Disc.pval","zds_Rep.pval","zds.repBF",
                    "SumSCRRB_SCequalRRB_Disc.r","SumSCRRB_SCequalRRB_Rep.r",
                    "SumSCRRB_SCequalRRB_Disc.pval","SumSCRRB_SCequalRRB_Rep.pval","SumSCRRB_SCequalRRB.repBF",
                    "zds_SCequalRRB_Disc.r","zds_SCequalRRB_Rep.r",
                    "zds_SCequalRRB_Disc.pval","zds_SCequalRRB_Rep.pval","zds_SCequalRRB.repBF",
                    "zds_SCoverRRB_Disc.r","zds_SCoverRRB_Rep.r",
                    "zds_SCoverRRB_Disc.pval","zds_SCoverRRB_Rep.pval","zds_SCoverRRB.repBF",
                    "VinelandABC_Disc.r","VinelandABC_Rep.r",
                    "VinelandABC_Disc.pval","VinelandABC_Rep.pval","VinelandABC.repBF",
                    "VinelandABC_SCequalRRB_Disc.r","VinelandABC_SCequalRRB_Rep.r",
                    "VinelandABC_SCequalRRB_Disc.pval","VinelandABC_SCequalRRB_Rep.pval",
                    "VinelandABC_SCequalRRB.repBF",
                    "VinelandABC_SCoverRRB_Disc.r","VinelandABC_SCoverRRB_Rep.r",
                    "VinelandABC_SCoverRRB_Disc.pval","VinelandABC_SCoverRRB_Rep.pval",
                    "VinelandABC_SCoverRRB.repBF")]
##           compNames SCequalRRB.repBF SCoverRRB.repBF SCcorr_Disc.r SCcorr_Rep.r
## IC01_IC12 IC01_IC12        0.7955174       0.4690143    0.02194196  0.003233085
## IC03_IC13 IC03_IC13       14.0854368       6.9485035   -0.21565303 -0.126963611
## IC04_IC11 IC04_IC11        0.7314191       0.7257347    0.05368490 -0.018351087
## IC04_IC12 IC04_IC12       16.1957454       0.5209997   -0.04308262  0.008786831
## IC05_IC06 IC05_IC06       80.0450180       1.4328289   -0.02776040  0.009072133
## IC05_IC19 IC05_IC19        0.8767820       1.8687764    0.08944708 -0.036191267
## IC07_IC13 IC07_IC13      737.3305480       9.8489366    0.02825143  0.056376570
## IC07_IC16 IC07_IC16        0.3237813       0.6219226    0.04398889 -0.018554199
## IC08_IC11 IC08_IC11        0.6878668       0.6957798    0.11614033  0.187959425
## IC08_IC20 IC08_IC20        1.4262907       0.6610975    0.06102096  0.200556463
## IC11_IC12 IC11_IC12        0.6891131      13.3782336   -0.09722395 -0.124975082
## IC12_IC17 IC12_IC17        4.5013271      11.5978596   -0.09815559 -0.203831211
## IC13_IC14 IC13_IC14      393.9080616       0.3826019   -0.05987545 -0.016340357
## IC14_IC20 IC14_IC20       12.5596603       2.0574500    0.10943481 -0.034469408
## IC15_IC17 IC15_IC17        0.4119129       0.3687863    0.03926970 -0.068463813
## IC17_IC18 IC17_IC18       16.2852363       1.4573308    0.12777685 -0.097275106
##           SCcorr_Disc.pval SCcorr_Rep.pval SCcorr.repBF RRBcorr_Disc.r
## IC01_IC12        0.6703983      0.87737433    0.6948175    0.022253885
## IC03_IC13        0.0334581      0.26358581    0.9932646   -0.100984042
## IC04_IC11        0.3378064      0.85020459    0.5092501    0.001223994
## IC04_IC12        0.7486846      0.65673363    0.6679560   -0.018878034
## IC05_IC06        0.5858669      0.88893298    0.6784365    0.129883126
## IC05_IC19        0.3018139      0.73140964    0.4593061    0.111258537
## IC07_IC13        0.7955791      0.78982547    0.7257912    0.006504303
## IC07_IC16        0.7642994      0.84887051    0.6716151    0.046963448
## IC08_IC11        0.1102083      0.02476101    8.0951621    0.140225485
## IC08_IC20        0.6297706      0.04189683    3.1180687    0.004305451
## IC11_IC12        0.3404468      0.21671088    1.4839549   -0.065262479
## IC12_IC17        0.2629685      0.02214415    7.0608340    0.268013929
## IC13_IC14        0.6899370      0.93721109    0.6627342   -0.033549747
## IC14_IC20        0.4673248      0.50302870    0.5366952    0.059448943
## IC15_IC17        0.9364592      0.28002795    0.9027455    0.059630717
## IC17_IC18        0.2831826      0.23107323    0.3932325    0.066930613
##           RRBcorr_Rep.r RRBcorr_Disc.pval RRBcorr_Rep.pval RRBcorr.repBF
## IC01_IC12  -0.031570729        0.67581534      0.662192433     0.7714689
## IC03_IC13  -0.105545423        0.32717839      0.330239493     1.1275916
## IC04_IC11  -0.006080479        0.39683381      0.489930085     0.8842338
## IC04_IC12  -0.132022831        0.91914190      0.201782973     1.1336710
## IC05_IC06   0.008273737        0.30770101      0.990641170     0.5386826
## IC05_IC19   0.020045801        0.23452312      0.904463122     0.5254590
## IC07_IC13   0.080865498        0.85975739      0.704722665     0.7454881
## IC07_IC16  -0.065311241        0.99053057      0.283640056     0.9349332
## IC08_IC11   0.234578549        0.08988736      0.022725465     8.8949014
## IC08_IC20   0.135383657        0.80035822      0.252352808     0.8340661
## IC11_IC12  -0.029488716        0.54328656      0.571609043     0.8210033
## IC12_IC17  -0.111746204        0.03543136      0.471909306     0.1191160
## IC13_IC14   0.240763309        0.94254345      0.005337477     4.5660604
## IC14_IC20  -0.196980403        0.89606118      0.038726525     1.8112695
## IC15_IC17   0.181375760        0.80383217      0.131115427     1.4936044
## IC17_IC18  -0.131431759        0.82941025      0.227572306     0.8830450
##           SumSCRRB_Disc.r SumSCRRB_Rep.r SumSCRRB_Disc.pval SumSCRRB_Rep.pval
## IC01_IC12      0.02901956    -0.01216495         0.59639342       0.744267295
## IC03_IC13     -0.21386179    -0.14306998         0.05019802       0.205371003
## IC04_IC11      0.03906505    -0.01646374         0.27528630       0.921735250
## IC04_IC12     -0.04208466    -0.05450550         0.78758276       0.723083828
## IC05_IC06      0.05825782     0.01057013         0.82961723       0.926953069
## IC05_IC19      0.13048683    -0.01768556         0.18012260       0.853706029
## IC07_IC13      0.02407416     0.07926642         0.78434455       0.712548753
## IC07_IC16      0.05957621    -0.04395750         0.82602984       0.540215720
## IC08_IC11      0.16676443     0.24768499         0.04649155       0.007703423
## IC08_IC20      0.04615055     0.21130671         0.86148346       0.044697063
## IC11_IC12     -0.10848355    -0.10658917         0.32474187       0.261342137
## IC12_IC17      0.09204190    -0.20289733         0.63770172       0.055301730
## IC13_IC14     -0.06286582     0.09966974         0.75457845       0.144709887
## IC14_IC20      0.11370131    -0.11684976         0.59080811       0.141479102
## IC15_IC17      0.06380923     0.03307955         0.82805483       0.970847557
## IC17_IC18      0.13122882    -0.13299202         0.38186762       0.159515783
##           SumSCRRB.repBF   zds_Disc.r    zds_Rep.r zds_Disc.pval zds_Rep.pval
## IC01_IC12      0.7302895  0.002657793  0.023309478   0.978621128   0.87424447
## IC03_IC13      1.3723820 -0.107756293 -0.062880426   0.320029498   0.66763968
## IC04_IC11      0.5458223  0.043233647 -0.014899531   0.876162830   0.55813481
## IC04_IC12      0.7446164 -0.022345781  0.092781160   0.845957187   0.15265506
## IC05_IC06      0.6864205 -0.112569212  0.004027948   0.193913203   0.88122595
## IC05_IC19      0.3920961 -0.003416208 -0.049681652   0.908621815   0.65702061
## IC07_IC13      0.7487993  0.018705713  0.006338976   0.934075525   0.99565767
## IC07_IC16      0.7123406  0.003703727  0.022444169   0.787040843   0.61519338
## IC08_IC11     22.7349100 -0.001563469  0.042788914   0.967658120   0.52097367
## IC08_IC20      2.3254318  0.047133160  0.119371439   0.543994106   0.19797173
## IC11_IC12      1.3174932 -0.034806562 -0.109081175   0.735765943   0.38135921
## IC12_IC17      1.0794209 -0.266888039 -0.137767387   0.005822396   0.06242402
## IC13_IC14      0.9382909 -0.026012431 -0.170165849   0.778505391   0.08031152
## IC14_IC20      0.7619345  0.048866197  0.090168120   0.588162711   0.39621295
## IC15_IC17      0.6886295 -0.008920903 -0.185111864   0.909308101   0.03988013
## IC17_IC18      0.5152166  0.058791490 -0.015996310   0.462311380   0.72393577
##           zds.repBF SumSCRRB_SCequalRRB_Disc.r SumSCRRB_SCequalRRB_Rep.r
## IC01_IC12 0.7030830                0.039682918              -0.053275570
## IC03_IC13 0.7055483               -0.207728240              -0.066990666
## IC04_IC11 0.7267282                0.049528691              -0.060997222
## IC04_IC12 1.0202064                0.057855158              -0.214238350
## IC05_IC06 0.5023456                0.080266276              -0.044488707
## IC05_IC19 0.7538494                0.237036965              -0.004206571
## IC07_IC13 0.6990597               -0.078614861               0.128479115
## IC07_IC16 0.7858125               -0.007431483              -0.043633285
## IC08_IC11 0.7680143                0.163775449               0.235325204
## IC08_IC20 1.4445651                0.109778625               0.225446130
## IC11_IC12 0.9614569               -0.169326009              -0.219868936
## IC12_IC17 3.1856173                0.133243166              -0.228755573
## IC13_IC14 1.9247746               -0.246526697               0.321690324
## IC14_IC20 0.9854177                0.086094875              -0.221042415
## IC15_IC17 2.3089681                0.010249859               0.144977010
## IC17_IC18 0.5526155                0.136020835              -0.136682732
##           SumSCRRB_SCequalRRB_Disc.pval SumSCRRB_SCequalRRB_Rep.pval
## IC01_IC12                    0.72856415                  0.969026260
## IC03_IC13                    0.13490716                  0.594070027
## IC04_IC11                    0.35400805                  0.905400209
## IC04_IC12                    0.77595768                  0.109790015
## IC05_IC06                    0.92727951                  0.712816095
## IC05_IC19                    0.14541166                  0.942388897
## IC07_IC13                    0.71800810                  0.341566696
## IC07_IC16                    0.68615507                  0.586163160
## IC08_IC11                    0.07597868                  0.089751588
## IC08_IC20                    0.62257597                  0.108824438
## IC11_IC12                    0.24469978                  0.096610878
## IC12_IC17                    0.33034995                  0.152731648
## IC13_IC14                    0.14032965                  0.007192758
## IC14_IC20                    0.73388593                  0.095970869
## IC15_IC17                    0.96644696                  0.346755585
## IC17_IC18                    0.84489336                  0.451813426
##           SumSCRRB_SCequalRRB.repBF zds_SCequalRRB_Disc.r zds_SCequalRRB_Rep.r
## IC01_IC12                 0.6745920          -0.277097910           0.01222164
## IC03_IC13                 0.6301976           0.045777149          -0.06574081
## IC04_IC11                 0.5331019          -0.067843384           0.01496057
## IC04_IC12                 1.0552972           0.005012945           0.02898635
## IC05_IC06                 0.7119774          -0.075535981           0.03980576
## IC05_IC19                 0.3825588           0.081871832           0.03859306
## IC07_IC13                 0.7189493          -0.122625417           0.09251081
## IC07_IC16                 0.8100174           0.132853464           0.26400831
## IC08_IC11                 3.0247951          -0.015949137           0.06642119
## IC08_IC20                 1.9093969           0.172856818          -0.07261177
## IC11_IC12                 2.6949289           0.092674386           0.07345652
## IC12_IC17                 0.4622104          -0.174141875          -0.25547619
## IC13_IC14                 0.3513133          -0.281095151           0.04072736
## IC14_IC20                 1.0462919           0.066592468           0.09615702
## IC15_IC17                 0.8632137           0.012884871          -0.28381070
## IC17_IC18                 0.7449407           0.020846790          -0.16083034
##           zds_SCequalRRB_Disc.pval zds_SCequalRRB_Rep.pval zds_SCequalRRB.repBF
## IC01_IC12               0.02129821              0.88587691            0.2054507
## IC03_IC13               0.60174683              0.66058325            0.6102065
## IC04_IC11               0.32899026              0.90791104            0.5822046
## IC04_IC12               0.91334822              0.67640506            0.7476418
## IC05_IC06               0.76594467              0.79662149            0.6694213
## IC05_IC19               0.54226522              0.67944038            0.7548410
## IC07_IC13               0.23895077              0.49990344            0.3685000
## IC07_IC16               0.14886315              0.01370250           12.2238649
## IC08_IC11               0.62487955              0.42212180            0.6386348
## IC08_IC20               0.04137936              0.55532623            0.1392755
## IC11_IC12               0.52653471              0.38330777            1.0154272
## IC12_IC17               0.21055635              0.01526879           10.1767698
## IC13_IC14               0.02330946              0.77042993            0.1313312
## IC14_IC20               0.57339998              0.48523399            0.8925094
## IC15_IC17               0.98227091              0.01234372            3.4568789
## IC17_IC18               0.45774195              0.09987684            0.6589320
##           zds_SCoverRRB_Disc.r zds_SCoverRRB_Rep.r zds_SCoverRRB_Disc.pval
## IC01_IC12           0.14054072       -0.0060153582            0.4314369522
## IC03_IC13          -0.34326125       -0.0749572171            0.0348879192
## IC04_IC11           0.18211389        0.0633555175            0.1602410613
## IC04_IC12          -0.09961334       -0.0044682949            0.7348721727
## IC05_IC06          -0.15658841        0.0519226751            0.4520965008
## IC05_IC19          -0.20957605       -0.0004208548            0.2954049687
## IC07_IC13           0.53192645        0.0557533273            0.0009321468
## IC07_IC16           0.04521700        0.0558002703            0.9977824883
## IC08_IC11          -0.27888369       -0.1777350289            0.1101459505
## IC08_IC20           0.05261409       -0.0184373130            0.8592543754
## IC11_IC12          -0.11466004       -0.0075125849            0.6512709114
## IC12_IC17          -0.53560195       -0.1655617227            0.0008085294
## IC13_IC14           0.05823789       -0.1726519419            0.8954054759
## IC14_IC20           0.14695973       -0.1854524594            0.3429652836
## IC15_IC17          -0.23401579       -0.4151432130            0.0853366697
## IC17_IC18           0.10168553       -0.1505652622            0.5516154764
##           zds_SCoverRRB_Rep.pval zds_SCoverRRB.repBF VinelandABC_Disc.r
## IC01_IC12             0.85516334          0.55898342        0.117928538
## IC03_IC13             0.56447437          0.42907591       -0.134772608
## IC04_IC11             0.75826752          0.53190112       -0.103254277
## IC04_IC12             0.98670955          0.68111452        0.002441248
## IC05_IC06             0.63920430          0.53475443        0.057668657
## IC05_IC19             0.93453906          0.55014177       -0.143747879
## IC07_IC13             0.64746484          0.06811246        0.073694498
## IC07_IC16             0.62154294          0.74628094        0.029103565
## IC08_IC11             0.21851790          1.46137709       -0.041406876
## IC08_IC20             0.93701212          0.69110033       -0.012128605
## IC11_IC12             0.96823868          0.65812655        0.056106901
## IC12_IC17             0.33518372          0.19293903        0.016795507
## IC13_IC14             0.25968014          0.90266897        0.025951362
## IC14_IC20             0.30938917          0.44372222       -0.182685897
## IC15_IC17             0.01308996         16.31276435       -0.025714298
## IC17_IC18             0.40088167          0.59413399       -0.162183284
##           VinelandABC_Rep.r VinelandABC_Disc.pval VinelandABC_Rep.pval
## IC01_IC12        0.17706471            0.20008779           0.06779140
## IC03_IC13        0.07548509            0.22800372           0.32023409
## IC04_IC11       -0.11207528            0.23611656           0.21170617
## IC04_IC12       -0.13179877            0.92509074           0.22292159
## IC05_IC06        0.03961356            0.73340254           0.75681897
## IC05_IC19       -0.21193640            0.13287524           0.02648763
## IC07_IC13       -0.04636066            0.52157295           0.52244583
## IC07_IC16       -0.01998481            0.63350953           0.86114208
## IC08_IC11        0.00709269            0.77280316           0.76425978
## IC08_IC20       -0.21462466            0.95294781           0.01721091
## IC11_IC12        0.05621054            0.55274527           0.47950555
## IC12_IC17        0.05329079            0.82865168           0.70524422
## IC13_IC14        0.02675679            0.68294229           0.73191706
## IC14_IC20       -0.21801342            0.04074162           0.01197059
## IC15_IC17       -0.09745959            0.65371858           0.23141424
## IC17_IC18       -0.06527082            0.08848605           0.38331532
##           VinelandABC.repBF VinelandABC_SCequalRRB_Disc.r
## IC01_IC12         3.5202257                    0.14464438
## IC03_IC13         0.3407267                   -0.19066515
## IC04_IC11         1.5352527                   -0.10451093
## IC04_IC12         0.9658800                   -0.07515700
## IC05_IC06         0.7347593                    0.12988458
## IC05_IC19         7.4442016                   -0.21402719
## IC07_IC13         0.5696654                    0.11911550
## IC07_IC16         0.6392246                   -0.12561640
## IC08_IC11         0.6716700                   -0.29797757
## IC08_IC20         3.2101596                   -0.10977147
## IC11_IC12         0.8988975                   -0.14229878
## IC12_IC17         0.7474660                   -0.11495606
## IC13_IC14         0.7420257                    0.01571556
## IC14_IC20        16.1724744                   -0.26680425
## IC15_IC17         1.2591897                    0.07376916
## IC17_IC18         0.8510854                   -0.09863034
##           VinelandABC_SCequalRRB_Rep.r VinelandABC_SCequalRRB_Disc.pval
## IC01_IC12                 0.0009291509                       0.32909942
## IC03_IC13                -0.0221926870                       0.23226235
## IC04_IC11                 0.0961412273                       0.41307359
## IC04_IC12                -0.1485553903                       0.62233264
## IC05_IC06                 0.1875840886                       0.43664151
## IC05_IC19                -0.3046567090                       0.10772391
## IC07_IC13                -0.0787165021                       0.50432242
## IC07_IC16                 0.0383986453                       0.58945283
## IC08_IC11                 0.1036129040                       0.03453314
## IC08_IC20                -0.3571061284                       0.56284984
## IC11_IC12                 0.0684858666                       0.27878247
## IC12_IC17                 0.0574014139                       0.54975165
## IC13_IC14                 0.0914518972                       0.72622361
## IC14_IC20                -0.2651582112                       0.04366782
## IC15_IC17                -0.0624815497                       0.78905427
## IC17_IC18                -0.0708768645                       0.45870215
##           VinelandABC_SCequalRRB_Rep.pval VinelandABC_SCequalRRB.repBF
## IC01_IC12                     0.912933526                   0.52046990
## IC03_IC13                     0.989972521                   0.48753775
## IC04_IC11                     0.509044927                   0.50214093
## IC04_IC12                     0.318471172                   1.09199286
## IC05_IC06                     0.150831159                   1.80317495
## IC05_IC19                     0.010860882                  15.81255120
## IC07_IC13                     0.418390930                   0.56243403
## IC07_IC16                     0.661490557                   0.60558343
## IC08_IC11                     0.262151007                   0.09098352
## IC08_IC20                     0.001848388                  20.11427182
## IC11_IC12                     0.374818376                   0.38903697
## IC12_IC17                     0.691654359                   0.59046050
## IC13_IC14                     0.361742943                   0.99009805
## IC14_IC20                     0.018087248                  11.96639023
## IC15_IC17                     0.441930920                   0.72343255
## IC17_IC18                     0.442115217                   0.94455160
##           VinelandABC_SCoverRRB_Disc.r VinelandABC_SCoverRRB_Rep.r
## IC01_IC12                   0.15885195                 0.540836261
## IC03_IC13                  -0.08587292                 0.172613120
## IC04_IC11                  -0.11135463                -0.454390194
## IC04_IC12                   0.08946024                -0.126702425
## IC05_IC06                  -0.02039252                -0.156913702
## IC05_IC19                  -0.10210360                -0.069523596
## IC07_IC13                   0.08133614                -0.012187289
## IC07_IC16                   0.18265234                -0.159445476
## IC08_IC11                   0.20768412                -0.189792284
## IC08_IC20                   0.09966423                 0.124610150
## IC11_IC12                   0.27610173                -0.033979460
## IC12_IC17                   0.17458268                -0.004087625
## IC13_IC14                   0.01658595                -0.140739306
## IC14_IC20                  -0.03275164                -0.054085220
## IC15_IC17                  -0.08710577                -0.159547623
## IC17_IC18                  -0.28114540                -0.054271484
##           VinelandABC_SCoverRRB_Disc.pval VinelandABC_SCoverRRB_Rep.pval
## IC01_IC12                      0.30607461                   0.0003568259
## IC03_IC13                      0.68633693                   0.2085142873
## IC04_IC11                      0.51023658                   0.0035422004
## IC04_IC12                      0.51076065                   0.4545966129
## IC05_IC06                      0.90445884                   0.2889182338
## IC05_IC19                      0.68173309                   0.8190018227
## IC07_IC13                      0.79295754                   0.8764272855
## IC07_IC16                      0.25650672                   0.2323850711
## IC08_IC11                      0.12403778                   0.3038445676
## IC08_IC20                      0.67986057                   0.5045923698
## IC11_IC12                      0.05145535                   0.8688910085
## IC12_IC17                      0.29708868                   0.9988669887
## IC13_IC14                      0.82302587                   0.4168627071
## IC14_IC20                      0.71867627                   0.6865425470
## IC15_IC17                      0.54438979                   0.2869280780
## IC17_IC18                      0.06078353                   0.6182177344
##           VinelandABC_SCoverRRB.repBF
## IC01_IC12                 140.8582916
## IC03_IC13                   0.7860256
## IC04_IC11                  17.9491075
## IC04_IC12                   0.5646209
## IC05_IC06                   0.9982184
## IC05_IC19                   0.7129276
## IC07_IC13                   0.6780060
## IC07_IC16                   0.3612854
## IC08_IC11                   0.2193664
## IC08_IC20                   0.8661881
## IC11_IC12                   0.2148602
## IC12_IC17                   0.5261220
## IC13_IC14                   0.7469726
## IC14_IC20                   0.7600222
## IC15_IC17                   1.1920280
## IC17_IC18                   0.4702474
#------------------------------------------------------------------------------
# Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

SCcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Disc_mat) = comps
colnames(SCcorr_Disc_mat) = comps
diag(SCcorr_Disc_mat) = 0

SCcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCcorr_Rep_mat) = comps
colnames(SCcorr_Rep_mat) = comps
diag(SCcorr_Rep_mat) = 0

RRBcorr_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Disc_mat) = comps
colnames(RRBcorr_Disc_mat) = comps
diag(RRBcorr_Disc_mat) = 0

RRBcorr_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(RRBcorr_Rep_mat) = comps
colnames(RRBcorr_Rep_mat) = comps
diag(RRBcorr_Rep_mat) = 0

SumSCRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Disc_mat) = comps
colnames(SumSCRRB_Disc_mat) = comps
diag(SumSCRRB_Disc_mat) = 0

SumSCRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SumSCRRB_Rep_mat) = comps
colnames(SumSCRRB_Rep_mat) = comps
diag(SumSCRRB_Rep_mat) = 0

zds_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Disc_mat) = comps
colnames(zds_Disc_mat) = comps
diag(zds_Disc_mat) = 0

zds_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_Rep_mat) = comps
colnames(zds_Rep_mat) = comps
diag(zds_Rep_mat) = 0


zds_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Disc_mat) = comps
colnames(zds_SCequalRRB_Disc_mat) = comps
diag(zds_SCequalRRB_Disc_mat) = 0

zds_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCequalRRB_Rep_mat) = comps
colnames(zds_SCequalRRB_Rep_mat) = comps
diag(zds_SCequalRRB_Rep_mat) = 0

zds_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Disc_mat) = comps
colnames(zds_SCoverRRB_Disc_mat) = comps
diag(zds_SCoverRRB_Disc_mat) = 0

zds_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(zds_SCoverRRB_Rep_mat) = comps
colnames(zds_SCoverRRB_Rep_mat) = comps
diag(zds_SCoverRRB_Rep_mat) = 0




VinelandABC_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Disc_mat) = comps
colnames(VinelandABC_Disc_mat) = comps
diag(VinelandABC_Disc_mat) = 0

VinelandABC_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_Rep_mat) = comps
colnames(VinelandABC_Rep_mat) = comps
diag(VinelandABC_Rep_mat) = 0


VinelandABC_SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Disc_mat) = comps
colnames(VinelandABC_SCequalRRB_Disc_mat) = comps
diag(VinelandABC_SCequalRRB_Disc_mat) = 0

VinelandABC_SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCequalRRB_Rep_mat) = comps
colnames(VinelandABC_SCequalRRB_Rep_mat) = comps
diag(VinelandABC_SCequalRRB_Rep_mat) = 0

VinelandABC_SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Disc_mat) = comps
colnames(VinelandABC_SCoverRRB_Disc_mat) = comps
diag(VinelandABC_SCoverRRB_Disc_mat) = 0

VinelandABC_SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(VinelandABC_SCoverRRB_Rep_mat) = comps
colnames(VinelandABC_SCoverRRB_Rep_mat) = comps
diag(VinelandABC_SCoverRRB_Rep_mat) = 0



for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (aovres[comp_pair,"SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"SCequalRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCequalRRB_Rep_vs_TD.pval"]<0.05){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"SCoverRRB_Disc_vs_TD.pval"]<0.05 & 
      aovres[comp_pair,"SCoverRRB_Rep_vs_TD.pval"]<0.05){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"SCcorr.repBF"]>10 & 
      aovres[comp_pair,"SCcorr_Disc.pval"]<0.05 & 
      aovres[comp_pair,"SCcorr_Rep.pval"]<0.05){
    SCcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Disc.r"]
    SCcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCcorr_Rep.r"]
  } else{
    SCcorr_Disc_mat[comp1,comp2] = 0.0001
    SCcorr_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"RRBcorr.repBF"]>10 & 
      aovres[comp_pair,"RRBcorr_Disc.pval"]<0.05 & 
      aovres[comp_pair,"RRBcorr_Rep.pval"]<0.05){
    RRBcorr_Disc_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Disc.r"]
    RRBcorr_Rep_mat[comp1,comp2] = aovres[comp_pair,"RRBcorr_Rep.r"]
  } else{
    RRBcorr_Disc_mat[comp1,comp2] = 0.0001
    RRBcorr_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"SumSCRRB.repBF"]>10 & 
      aovres[comp_pair,"SumSCRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"SumSCRRB_Rep.pval"]<0.05){
    SumSCRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Disc.r"]
    SumSCRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SumSCRRB_Rep.r"]
  } else{
    SumSCRRB_Disc_mat[comp1,comp2] = 0.0001
    SumSCRRB_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"zds.repBF"]>10 & 
      aovres[comp_pair,"zds_Disc.pval"]<0.05 & 
      aovres[comp_pair,"zds_Rep.pval"]<0.05){
    zds_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_Disc.r"]
    zds_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_Rep.r"]
  } else{
    zds_Disc_mat[comp1,comp2] = 0.0001
    zds_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"zds_SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"zds_SCequalRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"zds_SCequalRRB_Rep.pval"]<0.05){
    zds_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Disc.r"]
    zds_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCequalRRB_Rep.r"]
  } else{
    zds_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    zds_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"zds_SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"zds_SCoverRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"zds_SCoverRRB_Rep.pval"]<0.05){
    zds_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Disc.r"]
    zds_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"zds_SCoverRRB_Rep.r"]
  } else{
    zds_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    zds_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"VinelandABC.repBF"]>10 & 
      aovres[comp_pair,"VinelandABC_Disc.pval"]<0.05 & 
      aovres[comp_pair,"VinelandABC_Rep.pval"]<0.05){
    VinelandABC_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Disc.r"]
    VinelandABC_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_Rep.r"]
  } else{
    VinelandABC_Disc_mat[comp1,comp2] = 0.0001
    VinelandABC_Rep_mat[comp1,comp2] = 0.0001
  }

  if (aovres[comp_pair,"VinelandABC_SCequalRRB.repBF"]>10 & 
      aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.pval"]<0.05){
    VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Disc.r"]
    VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCequalRRB_Rep.r"]
  } else{
    VinelandABC_SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    VinelandABC_SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  }
  
  if (aovres[comp_pair,"VinelandABC_SCoverRRB.repBF"]>10 & 
      aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.pval"]<0.05 & 
      aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.pval"]<0.05){
    VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Disc.r"]
    VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"VinelandABC_SCoverRRB_Rep.r"]
  } else{
    VinelandABC_SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    VinelandABC_SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  }
      
}

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(RRBcorr_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SumSCRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))



col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(zds_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))








col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(VinelandABC_SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))



plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar

#------------------------------------------------------------------------------
# Consensus Chord diagram
ncomp_pairs = dim(aovres)[1]
comps = c("IC01","IC03","IC04","IC05","IC06","IC07","IC08","IC09","IC10","IC11","IC12","IC13","IC14","IC15","IC16","IC17","IC18","IC19","IC20")
ncomps = length(comps)

SCequalRRB_consensusPairs = c("IC07_IC13")
SCoverRRB_consensusPairs = c("IC12_IC17")

SCequalRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Disc_mat) = comps
colnames(SCequalRRB_Disc_mat) = comps
diag(SCequalRRB_Disc_mat) = 0

SCequalRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCequalRRB_Rep_mat) = comps
colnames(SCequalRRB_Rep_mat) = comps
diag(SCequalRRB_Rep_mat) = 0

SCoverRRB_Disc_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Disc_mat) = comps
colnames(SCoverRRB_Disc_mat) = comps
diag(SCoverRRB_Disc_mat) = 0

SCoverRRB_Rep_mat = matrix(nrow = ncomps, ncol = ncomps)
rownames(SCoverRRB_Rep_mat) = comps
colnames(SCoverRRB_Rep_mat) = comps
diag(SCoverRRB_Rep_mat) = 0

for (comp_pair in aovres$compNames){
  comp1 = substr(comp_pair,1,4)
  comp2 = substr(comp_pair,6,10)
  
  if (is.element(comp_pair,SCequalRRB_consensusPairs)){
    SCequalRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Disc_vs_TD.es"]
    SCequalRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCequalRRB_Rep_vs_TD.es"]
  } else{
    SCequalRRB_Disc_mat[comp1,comp2] = 0.0001
    SCequalRRB_Rep_mat[comp1,comp2] = 0.0001
  } # if
  
  if (is.element(comp_pair,SCoverRRB_consensusPairs)){
    SCoverRRB_Disc_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Disc_vs_TD.es"]
    SCoverRRB_Rep_mat[comp1,comp2] = aovres[comp_pair,"SCoverRRB_Rep_vs_TD.es"]
  } else{
    SCoverRRB_Disc_mat[comp1,comp2] = 0.0001
    SCoverRRB_Rep_mat[comp1,comp2] = 0.0001
  } # if

} # for

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "green", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "grey",
             IC13 = "green", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "grey",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")


col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCequalRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

grid.col = c(IC01 = "grey",
             IC03 = "grey",
             IC04 = "grey",
             IC05 = "grey",
             IC06 = "grey",
             IC07 = "grey", 
             IC08 = "grey",
             IC09 = "grey",
             IC10 = "grey",
             IC11 = "grey",
             IC12 = "blue",
             IC13 = "grey", 
             IC14 = "grey",
             IC15 = "grey",
             IC16 = "grey",
             IC17 = "blue",
             IC18 = "grey",
             IC19 = "grey",
             IC20 = "grey")

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Disc_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

col_fun = colorRamp2(c(-0.5, 0, 0.5), c("blue", "white", "red"))
circos.clear()
circos.par(gap.after = c(rep(10, ncomps)))
chordDiagram(SCoverRRB_Rep_mat, grid.col = grid.col, col=col_fun, annotationTrack = c("name","grid"))

plotdefault2 = data.frame(freq = seq(-0.5,0.5, length.out=100),y = as.factor(1))  
p_cbar = ggplot(data = plotdefault2, aes(x=freq,y=y)) +
  geom_tile(aes(fill=freq, alpha=0.5)) + 
  scale_fill_gradientn(colours=c("blue","white","red"), limits=c(-0.5,0.5), breaks=seq(-0.5,0.5,by=0.1)) +
  theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "none",
        axis.title.y=element_blank(),
        axis.title.x=element_blank(),
        axis.text.x=element_blank()) +
  coord_flip()
p_cbar